Smartphone-Based Methods and Systems

ABSTRACT

Methods and arrangements involving portable devices, such as smartphones and tablet computers, are disclosed. One arrangement enables a creator of content to select software with which that creator&#39;s content should be rendered—assuring continuity between artistic intention and delivery. Another arrangement utilizes the camera of a smartphone to identify nearby subjects, and take actions based thereon. Others rely on near field chip (RFID) identification of objects, or on identification of audio streams (e.g., music, voice). Some of the detailed technologies concern improvements to the user interfaces associated with such devices. Others involve use of these devices in connection with shopping, text entry, sign language interpretation, and vision-based discovery. Still other improvements are architectural in nature, e.g., relating to evidence-based state machines, and blackboard systems. Yet other technologies concern use of linked data in portable devices—some of which exploit GPU capabilities. Still other technologies concern computational photography. A great variety of other features and arrangements are also detailed.

RELATED APPLICATION DATA

This application claims priority benefit to the following provisionalpatent applications:

-   61/410,217, filed Nov. 4, 2010;-   61/449,529, filed Mar. 4, 2011;-   61/467,862, filed Mar. 25, 2011;-   61/471,651, filed Apr. 4, 2011;-   61/479,323, filed Apr. 26, 2011;-   61/483,555, filed May 6, 2011;-   61/485,888, filed May 13, 2011; and-   61/501,602, filed Jun. 27, 2011.

This application is also a continuation-in-part of application13/174,258, filed Jun. 20, 2011.

The disclosures of these previous applications are incorporated hereinby reference, in their entireties.

TECHNICAL FIELD

The present technology primarily concerns consumer electronic devices,such as smartphones and tablet computers.

INTRODUCTION

The present technology builds on work detailed in previous patentfilings. These include applications:

-   Ser. No. 13/149,334, filed May 31, 2011;-   Ser. No. 13/088,259, filed Apr. 15, 2011;-   Ser. No. 13/079,327, filed Apr. 4, 2011;-   Ser. No. 13/011,618, filed Jan. 21, 2011;-   Ser. No. 12/797,503, filed Jun. 9, 2010;-   Ser. No. 12/774,512, filed May 5, 2010;-   Ser. No. 12/716,908, filed Mar. 3, 2010 (published as 20100228632);-   Ser. No. 12/490,980, filed Jun. 24, 2009 (published as 20100205628);-   Ser. No. 12/271,772, filed Nov. 14, 2008 (published as 20100119208);-   Ser. No. 11/620,999, filed Jan. 8, 2007 (published as 20070185840);-   U.S. Pat. No. 7,003,731; and-   U.S. Pat. No. 6,947,571.

In the few years since their introduction, portable computing devices(e.g., smartphones, music players, and tablet computers) havetransitioned from novelties to near-necessities. With their widespreadadoption has come an explosion in the number of software programs(“apps”) available for such platforms. Over 300,000 apps are nowavailable from the Apple iTunes store alone.

Many apps concern media content. Some are designed to provide on-demandplayback of audio or video content, e.g., television shows. Others serveto complement media content, such as by enabling access to extra content(behind-the-scenes clips, cast biographies and interviews, contests,games, recipes, how-to videos), by allowing social network-basedfeatures (communicating with other fans, including by Twitter, Facebookand Foursquare, blogs), etc. In some instances a media-related app mayoperate in synchrony with the audio or video content, e.g., presentingcontent and links at time- or event-appropriate points during thecontent.

Apps are now being specialized to particular broadcast and recordedmedia content. The ABC television show My Generation, for example, wasintroduced with a companion iPad app dedicated exclusively to theprogram—providing polls, quizzes and other information in synchronizedfashion. Traditional media companies, such as CNN, ESPN, CBS, etc., areincreasingly becoming app companies as well.

It is difficult for apps to gain traction in this crowded marketplace.Searching iTunes, and other app stores, is the most common technique bywhich users find new apps for their devices. The next most populartechnique for app discovery is through recommendations from friends.Both approaches, however, were established when the app market was muchsmaller, and have not scaled well.

In the case of the My Generation iPad ap, for example, the show'sproducers must reach out to the target audience and entice them to go tothe app store, where they must type in the title of the app, downloadit, install it, and then run it when the television program is playing.

In accordance with certain embodiments of the present technology, adifferent solution is provided. In one such embodiment, amicrophone-equipped user device samples ambient content, and producescontent-identifying data from the captured audio. Thiscontent-identifying data is then used to look-up an app recommended bythe proprietor of the content, which app is then installed andlaunched—with little or no action required by the user.

By such arrangement, the content effectively selects the app. The userdoesn't select the software; the user's activity selects the software.Over time, each user device becomes app-adapted to the contentpreferences of the user—thereby becoming optimized to the user'sparticular interests in the content world.

To some degree, this aspect of the present technology is akin to therecommendation features of TiVo, but for apps. The user's contentconsumption habits (and optionally those of the user's social networkfriends) lead the device to recommend apps that serve the user'sinterests.

Desirably, it is artists that are given the privilege of specifying theapp(s) to be invoked by their creative works. Many countries have lawsthat recognize artists' continuing interest in the integrity with whichtheir works are treated (so-called “moral rights”). Embodiments of thepresent technology serve this interest—providing artists a continuingrole in how their art is presented, enabling them to prescribe thepreferred mechanisms by which their works are to be experienced.Continuity is provided between the artist's intention and the art'sdelivery.

It is not just stand-alone apps that can be treated in this fashion.More granular software choices can similarly be made, such as theselection of particular rendering codecs to be used by media players(e.g., Windows Media Player). For example, the National Hockey Leaguemay prefer that its content be rendered with a codec designed formaximum frame rate. In contrast, the Food Network may prefer that itscontent be rendered with a codec optimized for truest color fidelity.

Historically, the “channel” was king, and content played a supportingrole (i.e., drawing consumers to the channel, and to its advertising).From the consumer's standpoint, however, these roles should be reversed:content should be primary. Embodiments of the present technology arebased on this premise. The user chooses the content, and the deliverymechanism then follows, as a consequence.

The foregoing and other features and advantages of the presenttechnology will be more readily apparent from the following detaileddescription, which proceeds with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a system that can be used in certainembodiments of the present technology.

FIG. 2 is a representation of a data structure that can be used with theembodiment of FIG. 1.

FIGS. 3-7 detail features of illustrative gaze-tracking embodiments,e.g., for text entry.

FIGS. 8 and 9 detail features of an illustrative user interface.

FIG. 10 shows a block diagram of a system incorporating principles ofthe present technology.

FIG. 11 shows marker signals in a spatial-frequency domain.

FIG. 12 shows a mixed-domain view of a printed object that includes themarker signals of FIG. 11, according to one aspect of the presenttechnology.

FIG. 13 shows a corner marker that can be used to indicate hidden data.

FIG. 14 shows an alternative to the marker signals of FIG. 11.

FIG. 15 shows a graph representation of data output from a smartphonecamera.

FIG. 16 shows a middleware architecture for object recognition.

FIG. 17 is similar to FIG. 16, but is particular to the DigimarcDiscover implementation.

FIG. 18 is a bar chart showing impact of reading image watermarks onsystem tasks.

FIG. 19 further details performance of a watermark recognition agentrunning on an Apple iPhone 4 device.

FIG. 20 shows locations of salient points in first and second imageframes.

FIG. 21 shows histograms associated with geometric alignment of twoframes of salient points.

FIG. 22 shows an image memory in a smartphone, including three color bitplane of 8-bit depth each.

FIG. 23 shows a similar smartphone memory, but now utilized to store RDFtriples.

FIG. 24 shows some of the hundreds or thousands of RDF triples that maybe stored in the memory of FIG. 23.

FIG. 25 shows the memory of FIG. 23, now populated with illustrative RDFinformation detailing certain relationships among people.

FIG. 26 shows some of the templates that may be applied to the Predicateplane of the FIG. 25 memory, to perform semantic reasoning on thedepicted RDF triples.

FIG. 27 names the nine RDF triples within a 3×3 pixel block of memory.

FIG. 28 shows a store of memory in a smartphone.

FIGS. 29A and 29B depict elements of a graphical user interface thatuses data from the FIG. 28 memory.

FIG. 30 shows use of a memory storing triples, and associated tables, togenerate data used in generate a search query report to a user.

FIG. 31 shows another store of memory in a smartphone, depicting four ofmore planes of integer (e.g., 8-bit) storage.

DETAILED DESCRIPTION

The present technology, in some respects, expands on technology detailedin the assignee's above-detailed patent applications. The reader ispresumed to be familiar with such previous work, which can be used inimplementations of the present technology (and into which the presenttechnology can be incorporated).

Referring to FIG. 1, an illustrative system 12 includes a device 14having a processor 16, a memory 18, one or more input peripherals 20,and one or more output peripherals 22. System 12 may also include anetwork connection 24, and one or more remote computers 26.

An illustrative device 14 is a smartphone or a tablet computer, althoughany other consumer electronic device can be used. The processor cancomprise a microprocessor such as an Atom or A4 device. The processor'soperation is controlled, in part, by information stored in the memory,such as operating system software, application software (e.g., “apps”),data, etc. The memory may comprise flash memory, a hard drive, etc.

The input peripherals 20 may include a camera and/or a microphone. Theperipherals (or device 14 itself) may also comprise an interface systemby which analog signals sampled by the camera/microphone are convertedinto digital data suitable for processing by the system. Other inputperipherals can include a touch screen, keyboard, etc. The outputperipherals 22 can include a display screen, speaker, etc.

The network connection 24 can be wired (e.g., Ethernet, etc.), wireless(WiFi, 4G, Bluetooth, etc.), or both.

In an exemplary operation, device 14 receives a set of digital contentdata, such as through a microphone 20 and interface, through the networkconnection 24, or otherwise. The content data may be of any type; audiois exemplary.

The system 12 processes the digital content data to generatecorresponding identification data. This may be done, e.g., by applying adigital watermark decoding process, or a fingerprintingalgorithm—desirably to data representing the sonic or visual informationitself, rather than to so-called “out-of-band” data (e.g., file names,header data, etc.). The resulting identification data serves todistinguish the received content data from other data of the same type(e.g., other audio or other video).

By reference to this identification data, the system determinescorresponding software that should be invoked. One way to do this is byindexing a table, database, or other data structure with theidentification data, to thereby obtain information identifying theappropriate software. An illustrative table is shown conceptually inFIG. 2.

In some instances the data structure may return identification of asingle software program. In that case, this software is launched—ifavailable. (Availability does not require that the software be residenton the device. Cloud-based apps may be available.) If not available, thesoftware may be downloaded (e.g., from an online repository, such as theiTunes store), installed, and launched. (Or, the device can subscribe toa software-as-service cloud version of the app.) Involvement of the userin such action(s) can depend on the particular implementation: sometimesthe user is asked for permission; in other implementations such actionsproceed without disturbing the user.

Sometimes the data structure may identify several different softwareprograms. The different programs may be specific to different platforms,in which case, device 12 may simply pick the program corresponding tothat platform (e.g., Android G2, iPhone 4, etc.). Or, the data structuremay identify several alternative programs that can be used on a givenplatform. In this circumstance, the device may check to determinewhich—if any—is already installed and available. If such a program isfound, it can be launched. If two such programs are found, the devicemay choose between them using an algorithm (e.g., most-recently-used;smallest memory footprint; etc.), or the device may prompt the user fora selection. If none of the alternative programs is available to thedevice, the device can select and download one—again using an algorithm,or based on input from the user. Once downloaded and installed, theapplication is launched.

(Sometimes the data structure may identify different programs that servedifferent functions—all related to the content. One, for example, may bean app for discovery of song lyrics. Another may be an app relating tomusician biography. Another may be an app for purchase of the content.Again, each different class of software may include severalalternatives.)

Note that the device may already have an installed application that istechnically suited to work with the received content (e.g., to render anMPEG4 or an MP3 file). For certain types of operations, there may bedozens or more such programs that are technically suitable. However, thecontent may indicate that only a subset of this universe of possiblesoftware programs should be used.

Software in the device 14 may strictly enforce the content-identifiedsoftware selection. Alternatively, the system may treat such softwareidentification as a preference that the user can override. In someimplementations the user may be offered an incentive to use thecontent-identified software. Or, conversely, the user may be assessed afee, or other impediment, in order to use software other than thatindicated by the content.

Sometimes the system may decline to render certain content on a device(e.g., because of lack of suitable app or hardware capability), but mayinvite the user to transfer the content to another user device that hasthe needed capability, and may implement such transfer. (Ansel Adamsmight have taken a dim view of his large format photography being usedas a screen saver on a small format, low resolution, smartphone display.If such display is attempted, the software may invite the user toinstead transfer the imagery to a large format HD display at the user'shome for viewing.)

Instead of absolutely declining to render the content, the system mayrender it in a limited fashion. For example, a video might be renderedas a series of still key frames (e.g., from scene transitions). Again,the system can transfer the content where it can be more properlyenjoyed, or—if hardware considerations permit (e.g., screen displayresolution is adequate)—needed software can be downloaded and used.

As shown by the table of FIG. 2 (which data structure may be resident inthe memory 18, or in a remote computer system 26), the indication ofsoftware may be based on one or more contextual factors—in addition tothe content identification data. (Only two context factors are shown;more or less can of course be used.)

One formal definition of “context” is “any information that can be usedto characterize the situation of an entity (a person, place or objectthat is considered relevant to the interaction between a user and anapplication, including the user and applications themselves.”

Context information can be of many sorts, including computing context(network connectivity, memory availability, processor type, CPUcontention, etc.), user context (user profile, location, actions,preferences, nearby friends, social network(s) and situation, etc.),physical context (e.g., lighting, noise level, traffic, etc.), temporalcontext (time of day, day, month, season, etc.), history of the above,etc.

In the illustrated table, rows 32 and 34 correspond to the same content(i.e., same content ID), but they indicate different software should beused—depending on whether the user's context is indoors or outdoors.(The software is indicated by a 5 symbol hex identifier; the content isidentified by 6 hex symbols. Identifiers of other forms, and longer orshorter in length, can of course be used.)

Row 36 shows a software selection that includes two items ofsoftware—both of which are invoked. (One includes a furtherdescriptor—an identifier of a YouTube video that is to be loaded bysoftware “FF245.”) This software is indicated for a user in a daytimecontext, and for a user in the 20-25 age demographic.

Row 38 shows user location (zip code) and gender as contextual data. Thesoftware for this content/context is specified in the alternative (i.e.,four identifiers “OR”d together, as contrasted with the “AND” of row36).

Rows 40 and 42 show that the same content ID can correspond to differentcodecs—depending on the device processor (Atom or A4).

(By point of comparison, consider the procedure by which codecs arepresently chosen. Typically the user isn't familiar with technicaldistinctions between competing codecs, and the artist has no say. Codecselection is thus made by neither party that is most vitally interestedin the choice. Instead, default codecs come bundled with certain mediarendering software (e.g., Windows Media Player). If the defaults areunable to handle certain content, the rendering software typicallydownloads a further codec—again with no input from the parties mostconcerned.)

It will be understood that the software indicated in table 30 by thecontent can be a stand-alone app, or a software component—such as acodec, driver, etc. The software can render the content, or it can be acontent companion—providing other information or functionality relatedto the content. In some implementations the “software” can comprise aURL, or other data/parameter that is provided to another softwareprogram or online service (e.g., a YouTube video identifier).

Desirably, all such software identified in the table is chosen by theproprietor (e.g., artist, creator or copyright-holder) of the contentwith which it is associated. This affords the proprietor a measure ofartistic control that is missing in most other digital content systems.(The proprietor's control in such matters should be given more deferencethan, say, that of a content distributor—such as AOL or iTunes.Likewise, the proprietor's choice seems to merit more weight than thatof the company providing word processing and spreadsheet software forthe device.)

Often the proprietor's selection of software will be based on aestheticsand technical merit. Sometimes, however, commercial considerations comeinto play. (As artist Robert Genn noted, “‘Starving artist’ isacceptable at age 20, suspect at age 40, and problematical at age 60.”)

Thus, for example, if a user's device detects ambient audio by the groupThe Decemberists, artist-specified data in the data structure 30 mayindicate that the device should load the Amazon app for purchase of thedetected music (or load the corresponding Amazon web page), to inducesales. If the same device detects ambient audio by the Red Hot ChiliPeppers, that group may have specified that the device should load theband's own web page (or another app), for the same purpose. Theproprietor can thus specify the fulfillment service for contentobjected-oriented commerce.

In some arrangements, the starving artist problem may best be redressedby an auction arrangement. That is, the device 14 (or remote computersystem 26) may announce to an online service (akin to Google AdWords)that the iPod of a user—for which certain demographic profile/contextinformation may be available—has detected the soundtrack of the movieAvatar. A mini auction can then ensue—for the privilege of presenting abuying opportunity to the user. The winner (e.g., EBay) then pays thewinning bid amount into an account, from which it is shared with theauction service, the artist, etc. The user's device responds bylaunching an EBay app through which the user can buy a copy of themovie, its soundtrack, or related merchandise. Pushing such contentdetection events, and associated context information, to cloud-basedservices can enable a richly competitive marketplace of responses.

(Auction technology is also detailed in the assignee's previously-citedpatent applications, and in Google's published patent applicationsUS2010017298 and US2009198607.)

The popularity of content can lead associated software to becomesimilarly popular. This can induce other content proprietors to considersuch software for use with their own content, since wide deployment ofthat software may facilitate consumer exposure to the other proprietor'scontent.

For example, Universal Music Group may digitally watermark all its songswith an identifier that causes the FFmpeg MP3 player to be identified asthe preferred rendering software. Dedicated fans of UMG artists sooninstall the recommended software—leading to deployment of such softwareon large numbers of consumer devices. When other music proprietorsconsider what software to designate in table 30, the widespread use ofthe FFmpeg MP3 software can be one of the factors they weigh in making achoice.

(The software indicated in table 30 may be changed over time, such asthrough the course of a song's release cycle. When a new band issues asong, the table-specified software may include an app intended tointroduce the new band to the public (or a YouTube clip can be indicatedfor this purpose). After the music has become popular and the band hasbecome better known, a different software selection may be indicated.)

Presently, music discovery and other content-related applications arecommonly performed by application software. Operating system (OS)software provides a variety of useful services—some of which (e.g., I/O)are commonly used in content-related applications. However, commercialOS software has not previously provided any services specific to contentprocessing or identification.

In accordance with a further aspect of the present technology, operatingsystem software is provided to perform one or more services specific tocontent processing or identification.

In one particular implementation, an OS application programminginterface (API) takes content data as input (or a pointer to a locationwhere the content data is stored), and returns fingerprint datacorresponding thereto. Another OS service (either provided using thesame API, or another) takes the same input, and returns watermarkinformation decoded from the content data. (An input parameter to theAPI can specify which of plural fingerprint or watermark processes is tobe applied. Alternatively, the service may apply several differentwatermark and/or fingerprint extraction processes to the input data, andreturn resultant information to the calling program. In the case ofwatermark extraction, the resultant information can be checked forapparent validity by reference to error correction data or the like.)

The same API, or another, can further process the extractedfingerprint/watermark data to obtain XML-based content metadata that isassociated with the content (e.g., text giving the title of the work,the name of the artist, the copyright holder, etc.). To do this it mayconsult a remote metadata registry, such as maintained by Gracenote.

Such a content-processing API can establish a message queue (e.g., a“listening/hearing queue) to which results of the fingerprint/watermarkextraction process (either literally, or the corresponding metadata) arepublished. One or more application programs can monitor (hook) thequeue—listening for certain identifiers. One app may be alert to musicby the Beatles. Another may listen for Disney movie soundtracks. Whensuch content is detected, the monitoring app—or another—can launch intoactivity—logging the event, acting to complement the media content,offering a buying opportunity, etc.

Alternatively, such functionality can be implemented apart from theoperating system. One approach is with a publish/subscribe model, bywhich some apps publish capabilities (e.g., listening for a particulartype of audio), and other subscribe to such functions. By thesearrangements, loosely-coupled applications can cooperate to enable asimilar ecosystem.

One application of the present technology is to monitor media to which auser is exposed—as a background process. That is, unlike songidentification services such as Shazam, the user need not take anyaction to initiate a discovery operation to learn the identity of aparticular song. (Of course, the user—at some point—must turn on thedevice, and authorize this background functionality.) Instead, thedevice listens for a prolonged period—much longer than the 10-15 secondsof Shazam-like services, during the course of the user's day. As contentis encountered, it is processed and recognized. The recognitioninformation is logged in the device, and is used to prime certainsoftware to reflect exposure to such content—available the next time theuser's attention turns to the device.

For example, the device may process ambient audio for fifteen minutes,for an hour, or for a day. When the user next interacts with the device,it may present a listing of content to which the user has been exposed.The user may be invited to touch listings for content of interest, toengage in a discovery operation. Software associated with this contentthen launches.

In some implementations the device can prime software applications withinformation that is based, at least in part, on the contentidentification data. This priming may cause, e.g., the YouTube app toshow a thumbnail corresponding to a music video for a song heard by theuser—readying it for selection. Likewise, a 90 second sample audio clipmay be downloaded to the iPod music player app—available in a “RecentEncounters” folder. An email from the band might be added to the user'semail InBox, and a trivia game app may load a series of questionsrelating to the band. Such data is resident locally (i.e., the userneedn't direct its retrieval, e.g., from a web site), and theinformation is prominent to the user when the corresponding app is nextused—thereby customizing these apps per the user's content experiences.

Social media applications can serve as platforms through which suchinformation is presented, and shared. When the user activates a Facebookapp, for example, an avatar may give a greeting, “I noticed that youexperienced the following things today . . . ” and then list content towhich the user was exposed, e.g., “Billy Liar” by the Decemberists,“Boys Better” by the Dandy Warhols, and the new LeBron James commercialfor Nike. The app may remind the user of the context in which each wasencountered, e.g., while walking through downtown Portland on Nov. 4,2010 (as determined, e.g., by GPS and accelerometer sensors in thedevice). The Facebook app can invite the user to share any of thiscontent with friends. It may further query whether the user would likediscographies for any of the bands, or whether it would like fulldigital copies of the content, is interested in complementary contentassociated with any, or would like associated app(s) launched, etc.

The app may similarly report on media encounters, and associatedactivities, of the user's friends (with suitable permissions).

From the foregoing, it will be recognized that certain of the foregoingembodiments ease the user's dilemma of locating apps associated withcertain media content. Instead, the media content serves to locate itsown favored apps.

Such embodiments assure continuity between artistic intention anddelivery; they optimize the experience that the art is intended tocreate. No longer must the artistic experience be mediated by a deliveryplatform over which the artist has no control—a platform that may seekattention for itself, potentially distracting from the art in which theuser is interested.

This technology also fosters competition in the app marketplace—givingartists a more prominent voice as to which apps best express theircreations. Desirably, a Darwinian effect may emerge, by which apppopularity becomes less an expression of branding and marketing budgets,and more a reflection of popularity of the content thereby delivered.

Other Arrangements Filtering/Highlighting Data Streams by Reference toObject Interactions

Users are increasingly presented with large volumes of data. Examplesinclude hundreds of channels of television programming, email, andRSS/Twitter/social network/blog feeds. To help users handle such flowsof information, technologies have been proposed that filter or highlightthe incoming information in accordance with user profile data.

A familiar example is DVR software, such as from Tivo, that presents asubset of the unabridged electronic program guide, based on apparentuser interests. The Tivo software notices which television programs havebeen viewed by the user, invites user feedback in the form of“thumbs-up” or “thumbs-down” rankings, and then suggests future programsof potential interest based on such past behavior and ranking.

A more recent example is Google's “Priority Inbox” for its Gmailservice. Incoming email is analyzed, and ranked in accordance with itspotential importance to the user. In making such judgment, Googleconsiders what email the user has previously read, to which email theuser has previously responded, and the senders/keywords associated withsuch mails. Incoming email that scores highly in such assessment ispresented at the top of the mail list.

The company My6sense.com offers a similar service for triaging RSS andTwitter feeds. Again, the software monitors the user's historicalinteraction with data feeds, and elevates in priority the incoming itemsthat appear most relevant to the user. (In its processing of Twitterfeeds, My6sense considers the links the user has clicked on, the tweetsthe user has marked as favorites, the tweets that the user hasretweeted, and the authors/keywords that characterize such tweets.)

Such principles can be extended to encompass object interactions. Forexample, if a person visiting a Nordstrom department store uses hersmartphone to capture imagery of a pair of Jimmy Choo motorcycle boots,this may be inferred to indicate some interest in fashion, or inmotorcycling, or in footwear, or in boots, or in Jimmy Choo merchandise,etc. If the person later uses her smartphone to image River Roadmotorcycle saddle bags, this suggests the person's interest may moreaccurately be characterized as including motorcycling. As each new imageobject is discerned, more information about the person's interests isgleaned. Some early conclusions may be reinforced (e.g., motorcycling),other hypotheses may be discounted.

In addition to recognizing objects in imagery, the analysis (which caninclude human review by crowd-sourcing) can also discern activities.Location can also be noted (either inferred from the imagery, orindicated by GPS data or the like).

For example, image analysis applied to a frame of imagery may determinethat it includes a person riding a motorcycle, with a tent and aforested setting as a background. Or in a temporal series of images, oneimage may be found to include a person riding a motorcycle, anotherimage taken a few minutes later may be found to include a person in thesame garb as the motorcycle rider of the previous frame—now depictednext to a tent in a forested setting, and another image taken a fewminutes later may be found to depict a motorcycle being ridden with aforested background. GPS data may locate all of the images inYellowstone National Park.

Such historical information—accumulated over time—can reveal recurrentthemes and patterns that indicate subjects, activities, people, andplaces that are of interest to the user. Each such conclusion can begiven a confidence metric, based on the system's confidence that theattribute accurately characterizes a user interest. (In the examplesjust given, “motorcycling” would score higher than “Jimmy Choomerchandise.”) Such data can then be used in filtering or highlightingthe above-noted feeds of data (and others) with which the user's devicesare presented.

As a history of device usage is compiled, a comprehensive history ofinterests and media consumption patterns emerges that can be used toenhance the user's interaction with the world. For example, if the userimages fashion accessories from Sephora, parameters controlling theuser's junk email filter may be modified to allow delivery of emailsfrom that company—emails that might have otherwise have been blocked.The user's web browsers (e.g., Safari on the smartphone; Firefox on ahome PC) may add the Sephora web page to a list of “SuggestedFavorites”—similar to what Tivo does with its program suggestions.

A user may elect to establish a Twitter account that is essentiallyowned by the user's object-derived profile. This Twitter account followstweets relating to objects the user has recently sensed. If the user hasimaged a Canon SLR camera, this interest can be reflected in theprofile-associated Twitter account, which can follow tweets relating tosuch subject. This account can then re-tweet such posts into a feed thatthe user can follow, or check periodically, from the user's own Twitteraccount.

Such object-derived profile information can be used for more thaninfluencing the selection of content delivered to the user viasmartphone, television, PC and other content-delivery devices. It canalso influence the composition of such content. For example, objectswith which the user interacts can be included in media mashups for theuser's consumption. A central character in a virtual reality gamingworld frequented by the user may wear Jimmy Choo motorcycle boots.Treasure captured from an opponent may include a Canon SLR camera.

Every time the user interacts with an object, this interaction can bepublished via Twitter, Facebook, etc. (subject to user permission andsharing parameters). These communications can also be thought of as“check-ins” in the FourSquare sense, but in this case it is for anobject or media type (music, TV, etc.) rather than for a location.

Based on these public communiqués, social frameworks can emerge. Peoplewho are interested in hand-built Belgian racing bicycles from the 1980s(as evidenced by their capturing imagery of such bicycles) can coalesceinto an affinity social group, e.g., on Twitter or Facebook (or theirsuccessors). Object-based communities can thus be defined and exploredby interested users.

Social network theorists will recognize that this is a form of socialnetwork analysis, but with nodes representing physical objects.

Social network analysis views relationships using network theory, inwhich the network comprises nodes and ties (sometimes called edges,links, or connections). Nodes are the individual actors within thenetworks, and ties are the relationships between the actors. Theresulting graph-based structures can be complex; there can be many kindsof ties between the nodes. In the case just-given, the relationshipsties can include “likes,” “owns,” etc.

A particular 3D graph may place people objects in one plane, andphysical objects in a parallel plane. Links between the two planesassociate people with objects that they own or like. (The defaultrelationship may be “like.” “Owns” may be inferred from context, ordeduced from other data. E.g., a Camaro automobile photographed by auser, and geolocated at the user's home residence, may indicate an“owns” relationship. Similarly, a look-up of a Camaro license plate in apublic database, which indicates the car is registered to the user, alsosuggests an “owns” relationship.)

Such a graph will also typically include links between people objects(as is conventional in social network graphs), and may also includelinks between physical objects. (One such link is the relationship ofphysical proximity. Two cars parked next to each other in a parking lotmay be linked by such a relationship.)

The number of links to a physical object in such a network is anindication of the object's relative importance in that network. Degreesof association between two different physical objects can be indicatedby the length of the network path(s) linking them—with a shorter pathindicating a closer degree of association.

Some objects may be of transitory interest to users, while others may beof long-term interest. If a user images a particular type of object onlyonce, it likely belongs to the former class. If the user captures imagesof such object type repeatedly over time, it more likely belongs to thelatter class. Decisions based on the profile data can take into accountthe aging of object-indicated interests, so that an object encounteredonce a year ago is not given the same weight as an object encounteredmore recently. For example, the system may follow Canon SLR-based tweetsonly for a day, week or month, and then be followed no longer, unlessother objects imaged by the user evidence a continuing interest in Canonequipment or SLR cameras. Each object-interest can be assigned a numericprofile score that is increased, or maintained, by repeated encounterswith objects of that type, but which otherwise diminishes over time.This score is then used to weight that object-related interest intreatment of content.

(While the detailed arrangement identified physical objects by analysisof captured image data, it will be recognized that objects with whichthe user interacts can be identified otherwise, such as by detection ofRFID/NFC chips associated with such objects.)

Principles and embodiments analogous to those detailed above can beapplied to analysis of the user's audio environment, including music andspeech recognition. Such information can similarly be applied toselecting and composing streams of data with which the user (e.g., userdevice) is presented, and/or which may be sent by the user (userdevice). Still greater utility can be provided by consideration of bothvisual and auditory stimulus captured by user device sensors.

Text Entry

The front-facing camera on a smartphone can be used to speed text entry,in a gaze-tracking mode.

A basic geometrical reference frame can be first established by havingthe user look, successively, at 3 or 4 known positions on the displayscreen, while monitoring the gaze of one or both eyes using thesmartphone camera 101. In FIG. 3, the user looks successively at pointsA, B, C and D. Increased accuracy can be achieved by repeating thecycle. (The reader is presumed to be familiar with the principles ofgaze tracking systems, so same are not belabored here. Example systemsare detailed, e.g., in patent publications 20110013007, 20100315482,20100295774, and 20050175218, and references cited therein.)

Once the system has determined the geometrical framework relating theuser's eye gaze and the device screen, the user can indicate an initialletter by gazing at it on a displayed keyboard 102. (Other keyboarddisplays that make fuller use of the screen can of course be used.) Theuser can signify selection of the gazed-at letter by a signal such as agesture, e.g., an eye blink, or a tap on the smartphone body (or on adesk on which it is lying). As text is selected, it is added to amessage area 103

Once an initial letter (e.g., “N”) has been presented, data entry may bespeeded (and gaze tracking may be made more accurate) by presentinglikely next-letters in an enlarged letter-menu portion 104 of thescreen. Each time a letter is entered, a menu of likely next-letters(determined, e.g., by frequency analysis of letter pairs in arepresentative corpus) is presented. An example is shown in FIG. 4, inwhich the menu takes the form of a hexagonal array of tiles (althoughother arrangements can of course be used).

In this example, the user has already entered the text “Now is the timefor a_”, and the system is waiting for the user to select a letter to gowhere the underscore 106 is indicated. The last letter selected was “a.”This letter is now displayed—in greyed-out format—in the center tile,and is surrounded by a variety of options—including “an,” “at,” “al,”and “ar.” These are the four most common letter pairs beginning with“a.” Also displayed, in the indicated hexagonal array, is a “—”selection tile 108 (indicating the next symbol should be a space), and akeyboard selection tile 110.

To enter the next letter “l”, the user simply looks at the “al” displaytile 112, and signifies acceptance by a tap or other gesture, as above.The system then updates the screen as shown in FIG. 5. Here the messagehas been extended by a letter (“Now is the time for al_”), and the menu104 has been updated to show the most common letter pairs beginning withthe letter “l”. The device solicits a next letter input. To enteranother “l” the user gazes at the “ll” tile 114, and gestures.

Initial studies suggest that well over 50% of text entry can beaccomplished by the enlarged letter-menu of likely next-letters (plus aspace). If a different letter is required, the user simply gazes at thekeyboard tile 110 and gestures. A keyboard—like that shown in FIG. 3, oranother, appears, and the user makes a selection from it.

Instead of presenting four letter pairs, a space, and a keyboard icon,as shown in FIGS. 2 and 3, an alternative embodiment presents fiveletter pairs and a space, as shown in FIG. 6. In this arrangement, akeyboard is always displayed on the screen, so the user can selectletters from it without the intermediate step of selecting the keyboardtile 110 of FIG. 4.

Instead of the usual keyboard display 102, a variant keyboard display102 a—shown in FIG. 7—can be used. This layout reflects the fact thatfive characters are not needed on the displayed keyboard, since the fivemost-likely letters are already presented in the hexagonal menu. In theillustrated example, the five keys are not wholly omitted, but ratherare given extra-small keys. The 21 remaining letters are givenextra-large keys. Such arrangement speeds user letter selection from thekeyboard, and makes gaze tracking of the remaining keys more accurate.(A further variant, in which the five letter keys are omitted entirelyfrom the keyboard, can also be used.)

It will also be noted that the variant keyboard layout 102 a of FIG. 7omits the usual space bar. Since there is an enlarged menu tile 116 forthe space symbol, no space bar in the keyboard 102 a is required. In theillustrated arrangement, this area has been replaced with commonpunctuation symbols.

The artisan will recognize that numerous alternatives and extensions canbe implemented. There is no need, for example, for the last letter to bedisplayed in the center of the hexagon. This space can be left vacant,or can be used instead to indicate the next-letter apparently indicatedby the user's present gaze, so that the user can check the selectionbefore gesturing to confirm. (When updated with the apparently-indicatedletter, gazing at the center tile doesn't change the earlier gaze-basedselection.) A numeric pad can be summoned to the screen by selection ofa numeric pad icon—like keyboard tile 110 in FIG. 4. Or a numerickeyboard can be displayed on the screen throughout the messagecomposition operation (like keyboard 102 in FIG. 6). One or more of thehexagonal tiles can present a guess of the complete word the user isentering—again based on analysis of a text corpus.

The corpus used to determine the most common letter pairs, and full wordguesses, can be user-customized, e.g., a historical archive of all textand/or email messages authored by the user, or sent from the user'sdevice. The indicated display features can naturally be augmented byother graphical indicia and controls associated with the smartphonefunctionality being used (e.g., a text-messaging application).

In still other embodiments, the user may select from symbols and wordspresented apart from the smartphone display—such as on a printed page. Alarge-scale complete keyboard and a complete numeric pad can bepresented on such a page, and used independently, or in conjunction witha displayed letter menu, like menu 104. (Again, the smartphone cameracan be used to perform the gaze-tracking, and geometrical calibrationcan be performed by having the user gaze at reference points.)

Sign Language

Just as a smartphone can watch a user's eye, and interpret themovements, it can similarly watch a user's hand gestures, and interpretthem as well. The result is a sign language interpreter.

Sign languages (American and British sign languages being the mostdominant) comprise a variety of elements—all of which can be captured bya camera, and identified by suitable image analysis software. A signtypically includes handform and orientation aspects, and may also becharacterized by a location (or place of articulation), and movement.Manual alphabets (fingerspelling) gestures are similar, and are employedmostly for proper names and other specialized vocabulary.

An exemplary sign language analysis module segments thesmartphone-captured imagery into regions of interest, by identifyingcontiguous sets of pixels having chrominances within a gamut associatedwith most skin tones. The thus-segmented imagery is then applied to aclassification engine that seeks to match the hand configuration(s) witha best match within a database library of reference handforms. Likewise,sequences of image frames are processed to discern motion vectorsindicating the movement different points within the handforms, andchanges to the orientations over time. These discerned movements arelikewise applied to a database of reference movements and changes toidentify a best match.

When matching signs are found in the database, textual meaningsassociated with the discerned signs are retrieved from the databaserecords and can be output—as words, phonemes or letters—to an outputdevice, such as the smartphone display screen.

Desirably, the best-match data from the database is not output in rawform. Preferably, the database identifies for each sign a set ofcandidate matches—each with a confidence metric. The system softwarethen consider what combination of words, phonemes or letters is mostlikely in that sequential context—giving weight to the differentconfidences of the possible matches, and referring to a referencedatabase detailing word spellings (e.g., a dictionary), and identifyingfrequently signed word-pairs and—triples. (The artisan will recognizethat similar techniques are used in speech recognition systems—to reducethe likelihood of outputting nonsense phrases.)

The recognition software can also benefit by training. If the user notesan incorrect interpretation has been given by the system to a sign, theuser can make a sign indicating that a previous sign will be repeatedfor re-interpretation. The user then repeats the sign. The system thenoffers an alternative interpretation—avoiding the previousinterpretation (which the system infers was incorrect). The process maybe repeated until the system responds with the correct interpretation(which may be acknowledged with a user sign, such as a thumbs-upgesture). The system can then add to its database of reference signs thejust-expressed signs—in association with the correct meaning.

Similarly, if the system interprets a sign, and the user does notchallenge the interpretation, then data about the captured sign imagerycan be added to the reference database—in association with thatinterpretation. By this arrangement the system learns to recognize thevarious presentations of certain signs. The same technique allows thesystem to be trained, over time, to recognize user-specific vernacularand other idiosyncrasies.

To aid in machine-recognition, standard sign language can be augmentedto give the image analysis software some calibration or referenceinformation that will aid understanding. For example, when signing to asmartphone, a user may begin with gesture such as extending fingers andthumbs from an outwardly-facing palm (the typical sign for the number‘5’) and then returning the fingers to a fist. This allows thesmartphone to identify the user's fleshtone chrominance, and determinethe scale of the user's hand and fingers. The same gesture, or another,can be used to separate concepts—like a period at the end of a sentence.(Such punctuation is commonly expressed in American signal language by apause. An overt hand gesture, rather than the absence of a gesture, is amore reliable parsing element for machine vision-based sign languageinterpretation.)

As noted, the interpreted sign language can be output as text on thesmartphone display. However, other arrangements can also be implemented.For example, the text can be simply stored (e.g., in a ASCII or Worddocument), or it can be output through a text-to-speech converter, toyield audible speech. Similarly, the text may be input to a translationroutine or service (e.g., Google translate) to convert it to anotherlanguage—in which it may be stored, displayed or spoken.

The smartphone may employ its proximity sensor to detect the approach ofa user's body part (e.g., hands), and then capture frames of cameraimagery and check them for a skin-tone chrominance and long edges (orother attributes that are characteristic of hands and/or fingers). Ifsuch analysis concludes that the user has moved hands towards the phone,the phone may activate its sign language translator. Relatedly, Apple'sFaceTime communications software can be adapted to activate the signlanguage translator when the user positions hands to be imaged by aphone's camera. Thereafter, text counterparts to the user's handgestures can be communicated to the other party(ies) to which the phoneis linked, such as by text display, text-to-speech conversion, etc.

Streaming Mode Detector

In accordance with another aspect of the technology, a smartphone isequipped to rapidly capture identification from plural objects, and tomake same available for later review.

FIG. 8 shows an example. The application includes a large view windowthat is updated with streaming video from the camera (i.e., the usualviewfinder mode). As the user pans the camera, the system analyzes theimagery to discern any identifiable objects. In FIG. 8, there areseveral objects bearing barcodes within the camera's field of view.

In the illustrated system the processor analyzes the image framestarting at the center—looking for identifiable features. (In otherarrangements, a top-down, or other image search procedure can befollowed.) When the phone finds an identifiable feature (e.g., thebarcode 118), it overlays bracketing 120 around the feature, orhighlights the feature, to indicate to the user what part of thedisplayed imagery has caught its attention. A “whoosh” sound is thenemitted from the device speaker, and an animated indicia moves from thebracketed part of the screen to a History 122 button at the bottom. (Theanimation can be a square graphic that collapses to a point down at theHistory button.) A red-circled counter 124 that is displayed next to theHistory button indicates the number of items thus-detected and placed inthe device History (7, in this case).

After thus-processing barcode 118, the system continues its analysis ofthe field of view for other recognizable features. Working out from thecenter it next recognizes barcode 126, and a similar sequence ofoperations follows. The counter 124 is incremented to “8.” It next notesbarcode 128—even though it is partially outside the camera's field ofview. (Redundant encoding of certain barcodes enables such decoding.)The time elapsed for recognizing and capturing data from the threebarcodes into the device history, with the associated user feedback(sound and animation effects) is less than 3 seconds (with 1 or 2seconds being typical).

By tapping the History button 122, a scrollable display ofpreviously-captured features is presented, as shown in FIG. 9. In thislist, each entry includes a graphical indicia indicating the type offeature that was recognized, together with information discerned fromthe feature, and the time the feature was detected. (The time may bestated in absolute fashion, or relative to the present time; the latteris shown in FIG. 9.)

As shown in FIG. 9, the features detected by the system needn't be foundin the camera data. They can include features discerned from audio(e.g., identification of a person speaking), or from other sensors. FIG.9 shows that the phone also sensed data from a near field chip (e.g., anRFID chip)—indicated by the “NFC” indicia.

At a later time, the user can recall this History list, and tap indiciaof interest. The phone then responds by launching a responsecorresponding to that feature (or by presenting a menu of severalavailable features, from which the user can select).

Sometimes the user may wish to turn-off the detailed streaming modeoperation, e.g., when the environment is rich with detectable features,and the user does not want multiple captures. A button control 130 onthe application UI toggles such functionality on and off. In theindicated state, detection of multiple features is enabled. If the usertaps this control, its indicia switches to “Multiple is Off.” When thephone detects a feature in this mode, the system adds it to the History(as before), and immediately launches a corresponding response. Forexample, it may invoke web browser functionality and load a web pagecorresponding to the detected feature.

Evidence-Based State Machines, and Blackboard-Based Systems

Another aspect of the present technology involves smartphone-based statemachines, which vary their operation in response to sensor input.

application Ser. No. 12/797,503 details how a blackboard data structureis used for passing data between system components. The followingdiscussion provides further information about an illustrativeembodiment.

In this illustrative system, there are both physical sensors and logicalsensors. A physical sensor monitors a sensor, and feeds data from it tothe blackboard. The camera and microphone of a smartphone are particulartypes of physical sensors, and may be generically termed “mediasensors.” Some sensors may output several types of data. For example, animage sensor may output a frame of pixel data, and also an AGC(automatic gain control) signal.

A logical sensor obtains data—typically from the blackboard—and uses itto calculate further data. This further data is also commonly storedback in the blackboard. (The recognition agents discussed in applicationSer. No. 12/793,503 are examples of logical sensors. Another is aninference engine.) In some cases the same physical data may pass throughmultiple stages of logical sensor refinement during processing.

Modules which produce or consume media content may require some specialfunctionality, e.g., to allow format negotiation with other modules.This can include querying a recognition agent for its requested format(e.g., audio or video, together with associated parameters), and thenobtaining the corresponding sensor data from the blackboard.

Below follows a table detailing an exemplary six stage physicalsensor/logical sensor data flow, for reading a digital watermark fromcaptured imagery (the ReadImageWatermark scenario). The Stored Datacolumn gives the name of the stored data within the blackboard. (MSstands for media sensor; PS stands for physical sensor; LS stands forlogical sensor; and RA stands for recognition agent.)

Source Data Sensor Module Stored Data 1 Video frame Camera (MS)Data_Frame 2 Camera AGC Camera (MS) Data_AGC 3 Handset MovementAccelerometer (PS) Data_Jerk 4 Data_Frame Image Classifier (LS)Data_Classification 5 Data_AGC Watermark Inference Data_FrameQualityData_Jerk (LS) Data_Classification 6 Data_FrameQuality Watermark Reader(RA) Data_ReadResult Data_Frame Data_WM_ID

The first two lines simply indicate that a frame of video data, andassociated AGC data (which may be, e.g., an average luminance valueacross the frame), are written to the blackboard from the camera. Thethird line shows that associated handset movement data—as sensed by thesmartphone accelerometer system—is also written the blackboard.

In the fourth line, the table indicates that the Data_Frame data thatwas previously stored to the blackboard is applied to an imageclassifier (a variety of logical sensor), resulting in classificationdata that is stored in the blackboard. (Classification data can be ofvarious sorts. One type of classification data is color saturation. If aframe has very low color saturation, this indicates it is not a colorscene, but is more likely printed text on a white background, or abarcode. The illustrative data flow will not activate the watermarkdetector if the Data_Classification data indicates the scene is likelyprinted text or barcode—although in other implementations, watermarksmay be read from black and white, or greyscale, imagery. Anotherclassifier distinguishes spoken speech from music, e.g., so that a songrecognition process does not run when spoken audio is input.)

The fifth line indicates that the just-derived classification data,together with the AGC and accelerometer data, are recalled from theblackboard and applied to a watermark inference module (another logicalsensor) to yield a frame quality metric, which is written back to theblackboard. The watermark inference module uses the input data toestimate the likelihood that the frame is of a quality from which awatermark—if present—can be decoded. For example, if the AGC signalindicates that the frame is very dark or very light, then it isimprobable that a watermark is recoverable. Ditto if the accelerometerdata indicates that the smartphone is being accelerated when the frameof imagery was captured. (The accelerometer data is typicallycompensated for gravity.) Likewise if the classifier indicates it is alow-saturation set of data.

The sixth line shows that the just-determined frame quality metric isprovided—together with a frame of captured imagery—to a watermark reader(recognition agent). If the frame quality exceeds a threshold, thewatermark reader will attempt to decode a watermark from the imagery.The result of such attempt is stored in the ReadResult data (e.g., “1”indicates a watermark was successfully decoded; a “0” indicates that nowatermark was found), and the decoded watermark payload—if any—is storedas the WM_ID.

(In another embodiment, instead of conditionally invoking the watermarkdecoder if the frame quality metric exceeds a threshold, this metric canbe used as a priority value that dynamically controls operation of thewatermark decoder—based on system context. If the system is busy withother operations, or if other context—such as battery charge—makes thedecoding operation costly, then a frame with a low quality metric willnot be watermark-processed, so as not to divert system resources fromhigher-priority processes.)

In the illustrative system, the installed modules are enumerated in aconfiguration file. These modules are available for instantiation anduse at runtime. The configuration file also details one or morescenarios (e.g., ReadImageWatermark—as detailed above, andFingerprintAudio)—each of which specifies a collection of modules thatshould be used for that scenario. At runtime the application initializesmiddleware that specifies a particular scenario(s) to invoke. Themiddleware configuration, and scenarios, are typically loaded from anXML configuration file.

The illustrative system is coded in C/C++ (e.g., using Visual Studio2010), and follows the architecture shown in FIG. 10. Themiddleware—comprising the blackboard, together with an event controller,and a middleware state machine—is implemented in a dynamic link library(DLL).

As is familiar to artisans, the FIG. 10 system employs standardizedinterfaces through which different system components communicate. Inparticular, communication between system applications (above) and themiddleware is effected through APIs, which define conventions/protocolsfor initiating and servicing function calls. Similarly, the sensormodules (which are typically implemented as DLLs that are dynamicallyloaded at runtime by the middleware) communicate with the middlewarethrough a service provider interface (SPI).

The illustrated blackboard can store data in a variety of manners, e.g.,key-value pairs, XML, ontologies, etc. The exemplary blackboard storesdata as key-value pairs, and these are accessed using push and pullAPIs. Concurrency control is handled by pessimistic locking—preventingprocesses from accessing data while it is in use by another process. Theblackboard data types include data blobs in addition to discrete dataelements (e.g., integers and strings).

In addition to the data values themselves, each data entry has severalitems of associated data (metadata). These include:

-   -   Name    -   Source (name of the module that created the entry)    -   Value    -   Data type    -   Data size    -   Reference count    -   Timestamp (last update time)    -   Lifetime (how long this Value is useful)    -   Quality (how certain is this Value)

The values that are stored in the blackboard are of the followingrepresentative data types:

-   -   Video frames    -   Video statistics (frame rate, AGC, focal distance, etc.)    -   Accelerometer data    -   WM read result    -   WM ID    -   Video classification result

Data is written to and read from the blackboard through functions thatare supported by both the API and SPI. Such functions are familiar toartisans and include (with the parentheticals denoting values passed aspart of the API/SPI call):

-   -   BB_CreateEntry (name, source, type, size), returns Handle    -   BB_OpenEntry (name), returns Handle    -   BB_GetEntryNames (source, buffer)    -   BB_GetEntryInfo (name, source, info)    -   BB_GetEntryInfo (Handle, info)    -   BB_GetValue (Handle, value)    -   BB_SetValue (Handle, value)    -   BB_CloseEntry (Handle)

In addition to the above-detailed data types, the modules publish statusinformation to the blackboard using a common set of named entries. Eachname is created by using the pattern PREFIX+“_”+MODULE NAME. Theprefixes include:

-   -   Status (a numeric status code)    -   Error (an error string for the last error)    -   Result (a numeric result code of the most recent operation)

API and SPI functions also include Initialize, Uninitialize,LoadScenario, Start, Stop and Pause, through which the relevant DLLs areinitialized (or unitialized), and different scenarios are configured andstarted/stopped/paused.

The event controller module of the FIG. 10 middleware deals with thevarious priorities, processing complexity and processing frequency ofdifferent SPIs. For example, an image watermark decoder RA is processorintensive, but it operates on discrete frames, so frames can be ignoredif other SPIs need time to run. (E.g., in performing theWatermarkImageRead scenario on a stream of images, i.e., a video stream,various frames can be dropped—thereby scaling execution to the availableresources, and preventing the system from becoming bogged down). Bycontrast, an audio watermark decoder RA can be less processor intensive,but it needs to process audio data in an uninterrupted stream. That is,when a stream of audio data is available, the audio watermark RA shouldtake precedence over other SPIs. When multiple media streams (e.g.,audio and streaming images) are present, and other RAs are involved inprocessing, the event controller may periodically interrupt audioprocessing to allow image processing if a high quality frame of imageryis available.

Desirably, each module includes a data structure detailing informationabout the module's priority needs and limitations, execution frequency,etc. A sample of the data in such a structure follows:

-   -   Name    -   Type (PS, MS, LS, RA)    -   Priority (Low-High with, e.g., 2-10 steps)    -   Blackboard data values consumed    -   Blackboard data values produced

Modules and applications can further issue a blackboard triggerfunction, with a corresponding trigger value (or trigger value range),which causes the middleware (e.g., the blackboard or the eventcontroller) to issue such module/application a notification/message whencertain data in the blackboard meets the trigger value criterion. Onesuch trigger is if the ReadImageWatermark operation returns a ReadResultvalue of “1,” signifying a successful watermark read. Another trigger isif a music recognition module identifies the theme music to thetelevision show Grey's Anatomy). By such function, themodule/application can remain dormant until alerted of the presence ofcertain data on the blackboard.

By the foregoing arrangement, it will be recognized that each of thesensors can publish data and status information to the blackboard, andthis information can be retrieved and used by other modules and bydifferent applications which, in turn, publish their respective resultsto the blackboard. Through such iterative processing, raw data from asingle physical sensor can be successively processed and augmented withother information, and reasoned-with, to perform highly complexoperations. The ReadImageWatermark is a simple example of themulti-phase processing that such system enables.

More on Middleware, Etc.

FIG. 16 is another architectural view of middleware for media andphysical object recognition. This flexible architecture can also be usedto deliver more contextual information to the application. This designincludes a blackboard, a sensor bank, a recognition Agent (RA) bank, aninference engine, and an event controller.

As noted, the blackboard is central to the architecture. It is a sharedrepository through which system components communicate. No directcommunication is typically allowed among any other system components. Anexemplary blackboard is virtually structured into separate sections,each dedicated to a given type of data. For example, there is onesection for audio data and another for imagery.

The sensor bank provides inputs to the blackboard and may include acamera, microphone, accelerometer, gyroscope, ambient light sensor, GPS,etc. The RA bank may include image and audio watermark readers, afingerprint reader, a barcode reader, etc. Each sensor and RA contains aprivate knowledge base for estimating the cost of achieving its assignedtask and the quality of its results. This supports extensibility,enabling sensors or RAs to be added or removed with no impact on othercomponents of the system

The event controller coordinates the overall operation of the system.The inference engine monitors the content of the blackboard and inferscontextual data to optimize the use of the resources in the sensor andRA banks. The inference engine writes inferred data to the blackboard.

A minimum number of sensors is typically active to provide input to theblackboard. Upon input from any component, the blackboard may signal thechange to all components. Each sensor and RA then assesses whether itcan help resolve the identity of a detected object. A sensor can helpresolve the identity by providing relevant and more accurate data. Asensor or RA uses its knowledge base to estimate the cost and quality ofits solution and writes that data to the blackboard. The eventcontroller activates the sensor(s) or RA(s) estimated to produce optimalresults most economically. Sensors and the inference engine continue toupdate the blackboard to ensure that the most suitable module(s) isalways engaged. The system continues this process until the object'sidentity is resolved. In this scenario, the event controller optimizesthe use of power and resources in the system. For example, if thelighting level is poor or the device is in vigorous motion, the camerais not used and neither the image watermark reader nor the barcodereader is used for identification.

FIG. 17 shows the middleware architecture of the Digimarc Discoverapplication. It is implemented in C/C++ and assembly language andoptimized for the iPhone and Android platforms. The Digimarc Discoverapplication integrates RAs for digital watermarks, barcodes, and audiofingerprints.

The primary difference between the architecture depicted in FIG. 17 andthat shown in FIG. 16 is the absence of a formal inference engine, and amore limited role for the blackboard. Although some mechanisms areimplemented to decide whether to process a media sample, a fullinference engine is not required. Also, the blackboard is usedessentially as a means for moving media data (audio and video), alongwith some sensor data (accelerometer, gyroscope, etc.). The blackboardkeeps all captured data synchronized and queued for consumption by theRAs regardless of their sampling rates. When an RA is ready forprocessing, it requests a media sample from a corresponding data queuein the blackboard. The blackboard then provides the RA with the mediadata and associated sensor data.

When an RA begins processing the media sample, it may use any of thesensor data attached to the sample. The RA first decides whether toprocess the sample or not. It undertakes its identification task only ifit is relatively confident of reaching a correct identification.Otherwise, the RA aborts the operation and waits for the next mediasample. An RA may use a logical sensor to tune its identificationparameters. If successful, the RA returns its result to the applicationthrough the middleware.

To provide an attractive user experience, the RAs should quickly processlarge amounts of data for the best chance at a positive object/mediaidentification. Because identification requires such a volume of data,appropriate integration with the operating system is desirable. Thisintegration is typically tuned based on the particular audio and videocapturing process used. Without such integration, the blackboard may nothave enough data for the RAs to perform detection, and the chances ofobtaining a correct identification are reduced. Using multiple RAs atonce can exacerbate the problem.

An initial implementation of the Digimarc Discover application workedreasonably well as a demonstration platform, but it was not speedy, andwas not easily extensible. Integrating additional identificationtechnologies presented performance and implementation challenges. Toaddress such circumstance, two types of optimizations are implemented:

One is more efficient utilization of OS resources, through improvedintegration with the smartphone media capture subsystem. Another isiPhone-specific optimizations of the RAs. These are detailed below.

The iPhone version of the Digimarc Discover application relies onApple's Grand Central Dispatch threading facility, which permitslarge-scale multi-threading of the application with low thread latency.Audio and video streams are recorded in separate threads, and each RAruns in its own thread. Overhead did not observably increase with thenumber of threads. The benefits even on the iPhone's single-coreprocessor far outweigh possible drawbacks. RA processing is generallydriven by the audio and video capture threads, but this can varydepending on the type of RAs in use.

As noted earlier, there is a fundamental difference between streamingimage (video) identification technologies (e.g., watermarks andbarcodes), and audio (e.g., watermarks and fingerprints), namely thatvideo technologies process individual images, while audio technologiesprocess streams. Video is delivered to an application as a sequence offrames, each one a complete image. However audio is delivered as blocksof data in a raw byte stream. While a video frame can stand alone, asingle audio block is often useless for audio identification. Most audioidentification technologies need at least several seconds (equal to manyblocks) of audio data for an identification.

This difference between data types and sample rates causes differencesin middleware architecture. During high processor use, individual videoframes may be dropped with negligible effect on the robustness of thevideo RA. An audio RA, however, needs several blocks to identifycontent. The initial implementation of the Digimarc Discoverapplication, in which RAs processed media samples as they becameavailable, did not always work for audio. In some cases, audioprocessing could fall behind during heavy processing, leading to delayedresults, and a slower than desired user interface. To deal with this,the Digimarc Discover application employs a priority system in which themiddleware balances processing by throttling back RAs that can afford toskip frames, while keeping the others running at full speed.

Processing image/video frames is one of the most CPU-intensive tasks.Two separate RAs (watermark detector and barcode reader) could be usedsimultaneously to process each captured image. Excluding one RA fromexamining an image can significantly improve performance. As indicatedabove, the Digimarc Discover application uses a classifier to decidewhether the barcode reader should process an image. Since barcodes arenearly always printed in black and white, the classifier inspects thesaturation levels of images and excludes those with significant amountsof color. Similarly, a fingerprint-based Gracenote music recognition RAis controlled by reference to a speech classifier, which avoids callingthe Gracenote RA when microphone audio is classified as speech.

As indicated above, the ImageWatermarkRead scenario employs data fromthe smartphone accelerometer—preventing an attempted watermark read ifthe image would likely include excessive motion blur. Similarly, othersmartphone logical sensors, including other position/motion sensors, aswell as sensors of focal distance, automatic white balance, automaticgain control, and ISO, can be used to identify low quality frames, sothat smartphone resources are not needlessly consumed processing poorquality input.

Our work has shown that logical sensors help optimize the use of systemresources and enhance user experience. In like fashion, logical sensorsthat provide additional information about user context and devicecontext enable still more complex operations. Information about when,where, how, and by whom a device is used is desirably included in alldecisions involving the middleware. Some implementations employ a formalrepresentation of context, and an artificial intelligence-basedinference engine. In such arrangements, sensors and RAs may be conceivedas knowledge sources.

(Any logical sensor may be regarded, in a sense, as an inferencingmodule. A light sensor can detect low light, and infer the smartphone'scontext is in the dark. Such inference can be used to issue a signalthat turns on a torch to increase the illumination. A more sophisticatedarrangement employs several modules in the smartphone. For example, thelight sensor may detect low light, and the microphone may detect arustling noise. The system may infer the smartphone is in a user'spocket, in which case it may be pointless to turn on the camera's torch.Still more complex arrangements can employ one or more system modules,together with user history data and/or external resources. For example,the system may determine, by an audio fingerprinting module, an externaldatabase, and user history, that the smartphone user has watched 15minutes of Season 4, Episode 6, of the television show Grey's Anatomy,and that Sara Ramirez—the actress who plays surgeon Callie Tones—is oneof the user's favorite actresses, causing the smartphone to present alink to Ramirez's Wikipedia entry high in a list of menu optionsdisplayed to the user during this part of the episode.)

It will be recognized that the efficacy of recognition is highlyaffected by the speed of the RAs and the middleware. A variety ofimprovements can be taken in this regard.

Each RA can be modeled with a Receiver Operating Curve at a givenaggregate platform utilization profile. Ideally a mobile device profilefor each instance of an RA is employed to inform system design.

The Digimarc image watermark RA is optimized for the iPhone platform byimplementing the FFT, log-polar, and non-linear filtering stages inassembly language to take advantage of the NEON registers in theiPhone's A4 processor.

The image watermark RA processes 128×128-pixel blocks with a depth of 8bits, and it can run as a single-block or 4-block detector. In asingle-block embodiment, the execution time of the NEON implementationdecreased by 20% for both marked and unmarked frames, as shows as thetable below. Increased rejection speed for unmarked frames yields higherthroughput, which in turn increases the rate of recognition attempts bythe RA.

NEON Optimization (milliseconds) NEON (1 NEON (4 Baseline block) block)Unmarked  63 ms  50 ms 15-20 ms Marked 177 ms 140 ms   140 ms

The NEON implementation generates particular benefits when theregisters' SIMD capability is used to process 4 image blocksconcurrently. This 4-block approach enables use of a variety ofpre-filters to increase the operational envelope of the RA (discussedbelow) and thus improve the user experience.

Mobile platforms sometimes offer multiple APIs for a given systemservice. The choice of API can impact resource utilization and theresulting task mix on the platform. In some instances, the choice mayimpact the sensor's throughput.

In iOS 4.0, several APIs provide access to the camera. To identify thebest approach, an iPhone 4 was instrumented to capture the task mixwhile the Digimarc Discover application was used in the Print-to-Webusage model.

FIG. 18 shows the task mix before and after using the Preview API toretrieve image frames (builds 1.0.8 and 1.11, respectively). Using thePreview API dramatically reduces the time spent rendering the frame andfrees up time for image watermark recognition (shown as “Decoding WM”).Using the Preview API also allowed other system and application threadsmore use of the processor.

While affording the OS more time to service other threads is certainly abenefit to the system as a whole, the increase in throughput from 11 to14 frames per second is of more direct value to the user. The throughputincrease also increases the rate of attempts by the RA to recognize anobject.

To quantify improvements in user experience, recognition rates can becaptured as a function of first-order environmental factors, for aspecific user scenario. In addition, throughput can be measured tonormalize the recognition rates as a function of time.

The following table shows the results of using optimized RAs with boththe iOS 4.0 Preview and UIGetScreenImage APIs to retrieve frames fromthe iPhone Video Queue. The Preview API implementation yielded materialimprovements on an iPhone 3GS for all RAs.

Sustained FPS as Function of Number and Type of Image RA for iOS 3.0 &iOS 4.0 (iPhone3GS) iOS 3.0 UIGetScreenImage iOS 4.0 Preview RA: ImageWM 7 FPS   9 FPS RA: Barcode 5 FPS 8.5 FPS RA: Image WM + 4 FPS 5.1 FPSBarcode

For the Print-to-Web usage scenario, the primary environmental factorsare distance to the print, lighting, and pose. To study these factors, arobotic cell was built that repeatedly measures their impacts onrecognition rates.

Two versions of the image watermark RA were tested against distance. An“SS” version contains an improved sampling algorithm while a “Base”version does not. The sampling algorithm uses logical sensor dataprovided by CurrentFocusPosition in the metadata dictionary provided byiOS 4.2.

FIG. 19 displays the results for the two versions, showing which framesresulted in a successfully decoded watermark (or payload) and which didnot. The improved sampling algorithm materially increased the range ofdistances over which the watermark could be detected and the payloadrecovered.

Together, the results from FIG. 19 and the above table show thatcombining efficient utilization of system resources with optimized RAsmeasurably increases the operational envelope of the image watermark RA.

In sum, the detailed Digimarc Discover platform is designed, based onreal world usage scenarios, to provide content identification and reducethe associated complexity of building mobile discovery applications. Theplatform is architected to be extensible and allow the addition orremoval of any type of recognition agent without impacting the system.Efficient utilization of operating system resources, and optimization ofrecognition agents, allows consumer-pleasing system performance.Employing a middleware architecture that handles all audio and videocaptures reduces latency, facilitates sharing of system resources,reduces power consumption, and diminishes internal contentions. In someimplementations this middleware includes a formal inference engine thatadapts use of sensors and recognition agents based on user and devicecontext, while others use more informal types of inferencing.

Linked Data as an Atomic Construct of Mobile Discovery

As detailed in application Ser. No. 12/797,503, linked data principlescan be used in connection with smartphone data.

Smartphone sensors may be regarded as producing data about context. Oneway context data can be stored is by key-value pairs, comprising a label(generally taken from a dictionary) and a datum. (e.g., LAT=45.03;AudioEnvironment=SpokenWord; BatteryLeft=0.107). A drawback to suchsimple key-value expression is that the computer doesn't understand thelabels. It may simply know that the variable LAT conveys a floatingpoint number; the variable AudioEnvironment is a string, etc.

In accordance with another aspect of the present technology, suchinformation is represented in a semantically expressive manner, such asby a collection of data triples in the Resource Description Framework(RDF) knowledge representation language. (Triples typically comprise asubject, predicate (or property), and object.) The RDF schema (RDFS)allows maintenance of a hierarchy of objects and classes.

In triples, parameters can still be assigned values, but the triples aresemantically related to other information—imbuing them with meaning. Avariety of ontological models (RDFS, OWL, etc.) can be used to formallydescribe the semantics of these triples and there their relationship toeach other. The LAT parameter may still be assigned a floating pointdatum, but by reference to other triples, the computer can understandthat this LAT datum refers to a position on the Earth. Suchunderstanding allows powerful inferencing. (For example, a dataset thatplaces an object at latitude 45 at one instant of time, and at latitude15 two seconds later, can be understood to be suspect.) Semantic webtechnologies enable smartphones to reason based on contextualinformation and other data presented in such form.

Sensed context triples can be stored in graph form, where a sensor makesa collection of assertions about itself and its output data. One suchgraph (a tree) is shown in FIG. 15.

Different name-spaces can be used by different sensors, to reduce datacollisions. The distinct names can be reflected in each tripleassertion, e.g.:

{ImageSensor3DF12_ImageTime=2011040118060103_Pixel(0,0); HasRedValue;45} {ImageSensor3DF12_ImageTime=2011040118060103_Pixel(0,0);HasGreenValue; 32}

Etc. . . . .

Alternatively, a tree structure can include a unique name-spaceidentifier in a root or other fundamental node (as in FIG. 15), andother nodes can then be inferentially so-labeled. Trees have a longhistory as a data organizing construct, and a rich collection oftree-related techniques (e.g., sorting, pruning, memory optimization,etc.) can be applied.

A blackboard data structure can serve as a database for RDF triples. Inan extreme case, every pixel location in a captured image is expressedas one or more triples. In a particular implementation, sensor systemsare configured to output their data as streams of triples.

Predicates of triples may, themselves, be subjects of other triples. Forexample, “HasRedValue” is a predicate in the example above, but may alsobe a subject in a triple like {HasRedValue; IsAttributeOf; Image}. Asdata is streamed onto the blackboard, coalescing operations can beperformed—enabled by such understanding of data types.

Recognition agents, as detailed in application Ser. No. 12/797,503, canuse such data triples to trigger, or suspend, their operation. Forexample, if incoming pixel triples are dark, then no optical recognitionagents (e.g., barcode reader, watermark decoder, OCR engine) should berun. Expressing data in fine-grained fashion (e.g., down to the level oftriples asserting particular pixel values) allows similarly fine-grainedcontrol of recognition agents.

At a higher level of granularity, a triple may provide a pointer tomemory that contains a collection of pixels, such as a center 16×16pixel block within an image frame. Still higher, a triple may provide apointer to a memory location that stores a frame of imagery. Assertionsabout the imagery at this memory location can be made through a seriesof triples, e.g., detailing its size (e.g., 640×480 pixels), its colorrepresentation (e.g., YUV), its time of capture, the sensor with whichit was captured, the geolocation at which it was captured, etc.

Software (e.g., the ICP state machine in application Ser. No.12/797,503, with the middleware arrangements detailed above) canconsider the types of input data useful to different recognition agents,and can configure sensor systems to output data of different types atdifferent times, depending on context.

For example, context may indicate that both a barcode reading agent anda watermark decoding agent should be active. (Such context can include,e.g., geolocation in a retail store; ambient illumination that is abovea threshold, and the smartphone held in the user's hand.) The barcodereader may prefer luminance data, but less preferably could use RGB dataand derive luminance therefrom. The watermark decoder may require fullcolor imagery, but is indifferent whether it is provided in RGB, YUV, orsome other format. The system software can weigh the different needs andpreferences of the different recognition agents, and configure thesensor system accordingly. (In some embodiments, middleware serves as anegotiating proxy between different agents, e.g., solicitingpreference-scored lists of possible data types, scoring differentcombinations, and making a selection based on the resultant differentscores.)

In the just-noted case, the software would direct the sensor system tooutput YUV data, since such data is directly suitable for the watermarkdecoder, and because the Y channel (luminance) data can be directly usedby the barcode reader.

In addition to physical sensors, smartphones may be regarded as havinglogical sensors. Logical sensors may both consume context data, andproduce context data, and typically comprise software processes—eitheron the smartphone, or in the cloud. Examples run a wide gamut, from codethat performs early-stage recognition (e.g., here's a blob of pixelsthat appear to be related; here's a circular shape), to full-oninference driven sensors that report the current activity of the user(e.g., Tony is walking, etc.).

Such context data again can be stored as a simple graph, where thelogical sensor makes one or more assertions about the subject (e.g.,subject=Smartphone_Owner_Tony; predicate=Engaged_in_Activity;object=Walking). SPARQL can be used to access triples in the database,enabling detailed queries to be maintained.

Logical sensors can naturally use—as inputs—data other than smartphonesensor data and its derivatives. Sensors in the environment, forexample, can be sources of input. User calendar data or email data mayalso be used. (A sound sensed—or an objected viewed—at a time that theuser is scheduled to be in a meeting may be indicated as having occurredin the presence of the other meeting attendee(s).) Information obtainedfrom social media networks (e.g., via a Facebook or LinkedIn web API)can similarly be provided as input to a logical sensor, and be reflectedin an RDF output triple.

The recognition agents detailed in application Ser. No. 12/797,503 canembody state-machines and associated algorithms to recognize specificcontent/object types, in support of particular applications. Theapplications represent goal-driven usage models. They interface with thedetailed intuitive computing platform to perform specific tasks, byleveraging one or more recognition agents. E.g., decode a watermark,recognize a song; read a barcode. (The intuitive computing platformdetailed in application Ser. No. 12/797,503 uses sensors to generatecontext that can inform software agents—both local and in thecloud—about how to better complete their tasks.)

A particular implementation of this technology employs Jena—an opensource Java framework for semantic web applications (originallydeveloped by Hewlett-Packard) that provides an RDF API, reading/writingRDF/XML, N3, and N-triples, an OWL API, and a SPARQL query engine. Oneadaptation of Jena for mobile handsets is μ-Jena, from the Polytechnicof Milan. (Alternative implementations can use Androjena or Mobile RDF.)

The intuitive computing platform detailed in application Ser. No.12/797,503 manages traffic from applications to the recognition agents,and arbitrates resource contention of both logical and physical sensors.The blackboard data structure can be used to enable such inter-processcommunication, and maintain information about system status (e.g.,battery state).

An example of inter-process communication via the blackboard is awatermark decoder that senses inadequate luminance in captured imagery,and wants the smartphone torch to be turned-on. It may post a triple tothe blackboard (instead of making an OS system call) requesting suchaction. One such triple may be:

-   -   {Torch; Queued_Request; On)

Another may be

-   -   {WM_Decoder; Requests; Torch_On}

A torch control process (or another process) may monitor the blackboardfor such triples, and turn the torch on when same occur. Or, if batterypower is low, such a process may wait until two or more recognitionagents are waiting for the torch to be illuminated (or until otherindicia of urgency is found), and only then turn it on.

The watermark decoder may detect that the torch has been turned on by aSPARQL query that searches the blackboard for a triple indicating thatthe torch is powered. This query returns a response when the torch isilluminated, un-blocking the watermark decoding agent, and allowing itto run to completion.

Location is another important source of context information (asindicated above), and can similarly be expressed in terms of RDFtriples. DBpedia—a linked data expression of information from Wikipedia,and GeoNames, are among the many sources for such data. Phone sensordata (GPS) can be applied to the GeoNames or DBpedia services, to obtaincorresponding textual geo-labels.

The context data needn't derive from the user's own smartphone.Low-level sensor information (triples) collected/donated by others usingtheir mobile devices, e.g., in the same locale and time period can beused as well (subject to appropriate privacy safeguards). Likewise withdata from nearby stationary sensors, such as road cameras maintained bygovernment entities, etc. (The same locale is, itselfcontext/application dependent, and may comprise, e.g., within athreshold distance—such as 100 m, 1 km or 10 km; within the samegeographic entity—such as town or city; etc. Similarly, time-proximitycan be threshold-bounded, such as data collected within the past 10seconds, 10 minutes, hour, etc.). Such information can be directlyintegrated into the local blackboard so that device agents can operateon the information. In addition to imagery, such data can include audio,samples of wireless signals available in the area to help identifylocation, etc., etc. Such an “open world” approach to data sharing canadd enormously to the smartphone platform's understanding of context.

While the foregoing focuses on representation of context and sensor datain linked data fashion, other smartphone data can similarly benefit.

For example, in application 13/079,327, applicants detailed howmachine-readable data in printed text can be sensed and used to link toassociated information, both “tool tip” pop-up texts, and enlargedstores of related information. For example, when scanning a page ofnewspaper classified advertising with a camera-phone, the smartphonedisplay may present short synopses of advertisements as the phone passesover them (e.g., “1967 Mustang”). The particular newspaper being read iscontext information, and identifying issue information is deduced fromthe first watermark payload decoded from the page. From this context,appropriate pop-up texts for the entire newspaper are pulled from aremote data store, and cached on the phone for later use. Such pop-uptexts can be transmitted, and/or stored, in the form of triples (e.g.,{Watermark 14DA3; HasPopUpText; 1967 Mustang}).

Another implementation of linked data in smartphone applications isdetailed in Zander et al, “Context-Driven RDF Data Replication on MobileDevices,” submitted to Semantic Web Journal, 2010 (copy attached asAppendix A to application 61/485,888, which is incorporated by referenceherein). Although Zander's work is focused on context-informedreplication of structured Semantic Web data from remote sources tomobile devices, for use by local software agents, the detailed systemsillustrate other aspects of linked data utilization in smartphones.Features and details from Zander's work can be applied in connectionwith applicants' inventive work, and vice versa.

Mixed-Domain Displays

In accordance with another aspect of the present technology, asmartphone presents a display that includes both natural imagerycaptured by the camera, as well as transform-domain information (e.g.,in the spatial-frequency, or Fourier, domain) based on camera-capturedimagery.

application Ser. No. 12/774,512, filed May 5, 2010, details illustrativereference signals that can be encoded into imagery to aid asteganographic watermark detector in determining whether a watermark ispresent. The detailed reference signals are encoded in thespatial-frequency domain—at sufficiently high frequencies, and with achrominance—that causes them to be imperceptible to casual humanviewers.

Embodiments of the present technology reveal this transform domain-basedinformation to the viewer.

FIG. 11 shows an exemplary spatial-frequency domain view of a referencesignal 210 that is added to printed host imagery, with the realcomponents represented by the horizontal axis, and the imaginarycomponents represented by the vertical axis (the so-called “u,v” plane).The illustrated reference signal comprises pentagonal constellations 212of spatial domain impulses at frequencies (i.e., distances from theorigin) that are too high for humans to perceive, but that aredetectable in data produced by the image sensor in a smartphone camera.(The corresponding spatial-frequency domain view of the host imagery isnot shown, but would typically comprise signal scattered throughout theu,v plane, but mostly concentrated along the horizontal and verticalaxes.)

In the FIG. 11 view, the markers 215 are centered on a circle 215. Thelimit of human vision is shown by a smaller circle 217. Featurescomposed of spatial-frequency components outside of circle 217 (e.g.,markers 212) are too high in frequency to be discernible to humanviewers. (If the markers 212 were lower in spatial-frequency, they wouldcorrespond to a pixel pattern that is akin to a fine herringbone weave.At higher frequencies, however, the eye can't distinguish a weavepattern. Rather, the weave dissolves into apparent flatness.)

While four pentagonal marker constellations 212 are shown, of course alesser or greater number can also be used. Similarly, the markersneedn't be pentagonal in form.

When a smartphone camera detects reference pattern 210, it can therebydiscern the relative distance between the camera and the printed object,and any rotation and tilt of the camera relative to the object. Forexample, if the camera is moved closer to the object, the enlarged imagecomponents are sensed as having lower component spatial frequencies.Thus, the pentagonal markers move closer to the origin. If the camera isrotated (relative to the orientation at which the reference signal wasoriginally encoded in the host imagery), the pentagonal markers appearsimilarly rotated. If the camera is tilted—so that part of the printedimagery is closer to the sensor than other parts of the printedimagery—the pattern of pentagons is skewed. (No longer do their centers214 fall on a circle 215 centered about the u,v origin; instead, theyfall on an ellipse.)

FIG. 12 shows an exemplary smartphone display 220. In this illustration,the smartphone is imaging part of a cereal box—the artwork 222 of whichoccupies most of the screen. Superimposed on the screen is a half-planedepiction of the detected reference signal, including the top twopentagonal reference markers. The illustrated display also includes twofixed target regions 224—outlined in circular dashed lines. By movingthe phone towards or away from the cereal box, and tilting/rotating asnecessary, the user can cause the pentagonal markers 212 to move intothe two targeting regions 224. At this position, reading of thewatermark signal from the cereal box is optimized. The smartphone willread the watermark immediately (likely before the markers are aligned inthe targeting regions), and the phone will take a corresponding actionin response to the detected data.

Desirably, the transform domain overlay is presented at a visibility(strength) that varies with strength of the detected reference signal.If no reference signal is detected (e.g., by a detection metric outputby a pattern detector), then no overlay is presented. With strongersignals, the overlaid marker signals are presented with greatercontrast—compared to the background image 222. In some embodiments, themarkers are presented with coloration that varies in chrominance orluminosity, depending on strength of the detected reference signal.

In one particular implementation, the spatial-frequency representationof the captured imagery is thresholded, so that any spatial-frequencycomponent below a threshold value is not displayed. This prevents thedisplay from being degraded by a Fourier domain representation of thecaptured cereal box artwork 222. Instead, the only overlaid signalcorresponds to the marker signals.

Similarly, the spatial-frequency data may be high-passspectrally-filtered, so only image components that are above a thresholdspatial frequency (e.g., the spatial frequency indicated by circle 217in FIG. 11) are shown.

The circular target regions 224 are not essential. Other visual guidescan be presented, or they can be omitted entirely. In the latter case,the user may be instructed to position the phone so that the markers 224are even (i.e., horizontally-across). If the transformed data isspectrally-filtered (as described in the preceding paragraph), then theuser may be instructed to position the phone towards- or away-from thesubject until the markers just appear. (In actual practice, the fivepoints of the markers 212 look a bit like little pixie figures—a head,two hands and two feet, especially when rendered in color. The user canthus be instructed to “look for the pixie people.”Their appearance canbe made particularly noticeable by giving the five component elements ofeach marker different colors, and change the colors over time—yieldingan engaging, shimmering effect.)

In the particular embodiment depicted in FIG. 12, the spatial-frequencyinformation is shown in a rectangular box 226. In addition to serving asa frame for the spatial-frequency information, this box also serves todefine a rectangular sub-region of pixels within the artwork 222, onwhich the transform domain analysis is performed. That is, instead ofconverting the entire frame of imagery into the Fourier domain, onlythose pixels within the box 226 are so-converted. This reduces theburden on the phone processor. (The box 226 may be regarded as the fovearegion—the sub-region of pixels on which the processor focuses itsattention as it helps the user optimally position the phone.) Theluminance of pixels in region 226 can be slightly increased ordecreased—to further highlight the region to the user.

Watermark-Cueing Patterns

Digital watermarks are normally imperceptible. This is desirable becausethey can be encoded into fine artwork and other graphics withoutintroducing any visible change. However, this advantage has anassociated disadvantage: potential users of the encoded data areuncertain whether any watermarked data is present.

In the past, this disadvantage has sometimes been redressed by use of asmall visual logo, printed at a corner of the encoded visual artwork, toindicate that the artwork is watermark-encoded.

In accordance with another aspect of the present technology, thepresence of digitally watermarked information is visually cued by makingthe visual watermark pattern subtly visible.

As noted in the preceding section, if the spatial-frequency elementscomprising a watermark are low enough in frequency, they produce apattern akin to a weave (e.g., a herringbone weave, in the case ofregular pentagonal markers). In some applications, such a wovenbackground pattern is not objectionable. Background patterns arefamiliar from many contexts (e.g., on printed bank checks). Soespecially in the case of documents that don't include glossyphotographs, a pattern can be inserted without impairing the utility ofthe document.

Users can learn or be trained, over time, to recognize certain recurringpatterns as evidencing the presence of associated data. (Consider, forexample, how the presentation of blue underlined text in an on-linedocument is familiar to most users as signifying a hyperlink.)Smartphone-based systems can be used to capture imagery of suchdistinctive patterns, decode the watermarked information, and takecorresponding action(s).

In one particular embodiment, such a watermark includes a first set ofspatial frequency components that are within the range of human vision(i.e., inside circle 217 of FIG. 11), and a second set of spatialfrequency components that are beyond the range of human vision (i.e.,outside circle 217 of FIG. 11). The former can include components thatare pseudo-randomly distributed in the u,v plane to define acorresponding pattern in the pixel domain that is akin to the surfaceappearance of handmade paper—which commonly includes a random patternbased on the distribution of pulp fibers in such paper. This first setof spatial frequency components can be used repeatedly across all typesof documents—producing a characteristic pattern that users caneventually come to recognize as clueing the presence of encodedinformation. (The color of the pattern may be varied as best suits theapplication, by putting the spatial frequency components in differentcolor channels.) This consistent pattern can be used by the smartphonewatermark detector (1) to quickly identify the presence of a watermark,and optionally (2) to determine translation, scale and/or rotation ofthe captured imagery—relative to its originally encoded state.

The second set of spatial frequency components, in this particularembodiment, conveys some or all of the watermark payload information.This information varies from document to document. However, becausethese image components are not visible to humans in casual viewingcircumstances, such variability does not interfere with thecharacteristic texture pattern by which users recognize the document asincluding encoded information.

Just as colored, underlined text has become associated in people's mindswith hyperlinked information, so too can distinctive visible patternsbecome associated with the presence of digitally watermarkedinformation.

The clueing pattern may even take the form of a distinctive script ortypeface—used to indicate the presence of hidden information. Forexample, a font may include serifs that include a distinctive extensionfeature—such as a curl or twist or knot on the right side. Or, printingthat includes encoded watermark data may include a distinctive border.One is a framing rectangle defined by three fine lines. Another is a setof two- or four-similar corner markers—such as the one shown in FIG. 13.

(In some arrangements, such a border- or corner-marking is not presentin the original physical medium, but is rendered as an on-screen graphicoverlay that is triggered by smartphone detection of a signal (e.g., theFIG. 11 or 14 signal) encoded in the medium. In a particulararrangement, the lines of such overlaid marking are rendered in asomewhat blurred fashion if the smartphone is at a sub-optimal viewingpose, and are increasingly rendered in-focus as the user moves thesmartphone to a more optimum viewing pose. When the phone is positionedoptimally (e.g., with plan view of the watermarked subject, at adistance of six inches), then the lines are presented in crisp, sharpform. Thus, software in the phone translates information about theoptimality of the viewing pose into a visual paradigm that is somewhatfamiliar to certain users—the dependence of focus on distance.)

Layers of Information Presentation

In some implementations, there may be three conceptual “layers” throughwhich information is presented to a user. These may be termed thevisual, flag, and link layers.

The visual layer is a human-perceptible clue that there is digitalwatermark information present. As just-noted, these can take differentforms. One is a logo, typeface, border, or other printed indicia thatindicates the presence of encoded information. Another is a visibleartifact (e.g., weave-like patterning) that is introduced in printedcontent as part of the watermarking process.

The flag layer is an indicia (typically transitory) that is presented tothe user as a consequence of some initial digital image processing. Oneexample is the “pixie people” referenced above. Another is the“proto-baubles” discussed in application Ser. No. 12/797,503. Others arediscussed in application Ser. No. 12/774,512. The flag layer serves as afirst glimmer of electronic recognition that there is, in fact, awatermark present. (The flag layer may optionally serve as an aid toguide the user in positioning the smartphone camera for an optimizedwatermark read.)

The link layer comprises the information presented to the user after thewatermark is decoded. This commonly involves indexing a resolverdatabase with a decoded watermark payload (e.g., a large number) tolearn what behavior is associated with that watermark, and theninitiating that behavior.

Encoded Data Translation

In accordance with a further aspect of the present technology, devicesthat receive watermark-encoded media signals can act to decode thewatermark data, and relay it onward by another data channel.

Consider a television, set-top box, or other device that receives videoentertainment programming. The audio and/or video of the programming maybe encoded with digital watermark information, e.g., that identifies theprogram. A consumer may be using a smartphone or tablet computer whilewatching the video programming on the television, and it may beadvantageous for the smartphone/computer to know the identity of theprogram being viewed (e.g., for reasons detailed in patent publications20100119208 and 20100205628). In the prior art, this has beenaccomplished—for watermarks encoded in program audio—by capturingambient audio using a microphone in the smartphone or computer, and thendecoding the watermark data from the captured audio. However, this issometimes made difficult by other sounds that may also be captured bythe microphone and that may interfere with reliable watermark decoding.

In accordance with this aspect of the present technology, a first device(e.g., a television or set-top box) decodes watermark data from acontent stream. It then relays this data—by a different channel—to asecond device (e.g., a smartphone).

In one illustrative embodiment, a decoder in a television receivesprogramming and decodes, from the audio component, an audio watermark.It then re-transmits the decoded watermark data to nearby smartphonesvia Bluetooth wireless technology. These smartphones thus receive thewatermark data (using their built-in Bluetooth receivers) free ofambient room noise interference.

Another wireless data channel by which decoded watermark information canbe relayed is the NFC radio protocol (which presently operates at 13.56MHz). Although NFC systems typically include a receiver (e.g., asmartphone) that acts to power a nearby passive NFC chip/emitter bymagnetic coupling, and then receive a resulting weak RF response emittedby the chip, the same smartphone NFC circuitry can receive signals thatare transmitted by a powered 13 MHz transmitter—with which a television,set-top box, or other device may be equipped. The lowest standard NFCdata rate, 106 kbits/second, is more than adequate forwatermark-relating service (and is sufficiently broadband to allowhighly redundant error-correction coding of the relayed data—ifdesired).

Still another data channel for relaying decoded watermark data betweendevices is WiFi, e.g., according to the 802.11b, 802.11g, or 802.11nstandards.

Yet another data channel is IR communications—such as the sort by whichtelevisions and remote controls commonly communicate. In thisapplication, however, the television (or set-top box, etc.) is typicallythe emitter of the IR radiation, rather than the receiver. IRcommunications systems commonly use a wavelength of 940 nm. The data iscommunicated by modulating a carrier signal, e.g., 36 KHz, in the caseof the popular RC-5 protocol. In this protocol, each button on a remotecontrol corresponds to a 14-bit code transmission, with which thecarrier signal is modulated when the button is pressed. Watermark datacan be conveyed in similar fashion, e.g., by using groups of 14-bitcodes (thereby allowing existing decoding hardware to be adapted forsuch use).

In one particular system, the television (or set-top box) advertises—toother devices—the availability of decoded watermark data using theBonjour service. As detailed in publication 20100205628, Bonjour is animplementation of Zeroconf—a service discovery protocol. Bonjour locatesdevices on a local network, and identifies services that each offers,using multicast Domain Name System service records. This software isbuilt into the Apple MAC OS X operating system, and is also included inthe Apple “Remote” application for the iPhone, where it is used toestablish connections to iTunes libraries via WiFi. Bonjour is also usedby TiVo to locate digital video recorders and shared media libraries.Using Bonjour, the first device advises other devices on the network ofthe availability of the watermark data, and provides parameters allowingthe other devices to obtain such data.

The foregoing principles can also be employed in connection with mediafingerprints (rather than watermarks). A first device (e.g., atelevision or set-top box) can derive fingerprint data from receivedmedia content, and then communicate the fingerprint data to a seconddevice via another data channel. (Alternatively, the first device maysend the fingerprint data to a database system. The database systemtries to find a close match among stored reference data, to therebyaccess metadata associated with the fingerprint-identified content. Thismetadata can then be sent back to the originating first device. Thisfirst device, in turn, relays this metadata on to the second device viathe data channel.)

Smartphone-Aided Personal Shopping Service

In accordance with still another aspect of the present technology, asmartphone is used in connection with a personal shopping service.

Consider a service-oriented retail establishment—such as the Applestores found in certain shopping districts. A consumer browsing in sucha store may use a smartphone to express curiosity about a product (e.g.,a MacBook Pro computer). This may involve capturing an image of theMacBook Pro, or otherwise sensing identification information (e.g., froman RFID or NFC chip on the device, or from a barcode or watermark onassociated signage). The smartphone sends a signal to a serviceindicating the consumer's interest. For example, the phone maywirelessly (e.g., by WiFi or Bluetooth) send the image, or the sensedidentification information, to a back office store computer that isrunning shopper service application software.

With the transmitted product information, the phone also sends to theback office computer an identifier of the consumer. This consumeridentifier can be a name, telephone number, Apple customer number (e.g.,iTunes login identifier), or Facebook (or other social network) loginidentifier, etc. The shopper service application software then retrievesprofile information, if any, associated with that shopper. This profileinformation can include the person's history with Apple—includingpurchasing history, a list of registered Apple software, and informationabout other shopper-Apple encounters.

The shopper service application software enters the consumer in a queuefor personal service. If there are several customers ahead in the queue,the software predicts the wait time the shopper will likely experiencebefore service, and sends this information to the consumer (e.g., by atext message to the user's phone).

If there will be a delay before the store can assign a personal shoppingassistant to the customer, the store may provide the customer (e.g., thecustomer's smartphone or other computer device) with engaging content tohelp pass the time. For example, the store may grant the shopperunlimited listening/viewing rights to songs, video and other mediaavailable from the iTunes media store. Free downloads of a limitednumber of content items may be granted. Such privileges may continuewhile the shopper remains in or near the store.

When a personal shopping assistant is available to help the customer,the software sends the shopper an alert, including the assistant's name,and a picture of the assistant. Previously, a distilled version of theshopper's profile information—giving highlights in abbreviated textualform—was provided to the shopping assistant (e.g., to the assistant'ssmartphone), to give background information that may help the assistantprovide better service. The assistant then approaches the customer, andgreets him or her by name—ready to answer any questions about theMacBook Pro.

The queue for personal service may not be strictly first-come,first-served. Instead, shoppers with a history of Apple purchases may begiven priority—and bumped ahead of others in the queue, in accordancewith the value of their past Apple purchases. The shopper servicesoftware applies some safeguards to assure that new customers are notalways bumped down in priority each time an existing Apple customerenters the store. For example, the queue may be managed so that alimited number of priority customers (e.g., two) is granted placement inthe queue ahead of a new customer. After two priority customers arebumped ahead of the new customer, the next priority customer is insertedin the queue after the new customer (but ahead of other new customerswho have not yet been twice-bumped).

Queue management can depend on factors in addition to (or other than)past transaction history with Apple. Mining of public and commercialdatabases allows compilation of useful demographic profile informationabout most shoppers. If the shopper service computer determines that acustomer who just entered the store appears to be the DMV registrant ofa late-model Lexus automobile, that customer may be given a priorityposition in the queue ahead of an earlier customer who, DMV recordsindicate, drives an old Yugo. (Or, the store may adopt the oppositepolicy.)

In addition to managing customer service, in part, based on Appletransactional information, and on data gleaned from public andcommercial databases, such decisions can be similarly based oninformation voluntarily provided by the customer. For example, “digitalwallet” technology allows individuals to easily share certaindemographic and other profile information about themselves, from theirsmartphone or other device, to others—including to retailestablishments. A customer's position in a customer service queue may bebased on such self-revealed information. Consumers may find that, themore information they make available about themselves, the bettercustomer service they are provided.

The foregoing functionality may be implemented via an applicationprogram downloaded to the customer's smartphone, or as a web service towhich the customer is directed. Or, much of the functionality may beimplemented by text (picture) messaging arrangements—with the storeoptionally providing links that invoke other standard smartphonesoftware (e.g., a web browser or iTunes software).

Convenient Compatibility Determinations

In accordance with a further aspect of the present technology, asmartphone is used to quickly identify accessories that are useful withcertain electronic devices.

An illustrative scenario is a shopper who enters an electronicsretailer, such as Fry's, looking for a protective case for her HTCThunderbolt smartphone. The store has a wall of smartphone cases. In theprior art, the shopper would scrutinize each different package—lookingfor an indication of the smartphone(s) for which that case is suited.This may require removing many of the cases from the wall and turningthe packages over—reading fine print. Frustration quickly ensues.

In accordance with this aspect of the present technology, the retailermakes available a software tool, which may be downloaded to the user'ssmartphone (or other device). Or the tool may be offered as a webservice. The user is invited to indicate what they are looking for, suchas by a dropdown menu that may include Accessories (cases, chargers,etc.). When the user selects “Accessories,” a further dialog inquiresabout the product for which accessories are sought. The user enters (orselects from a dropdown menu) “HTC Thunderbolt.” (The artisan willrecognize that this information may be gleaned in many other ways—theparticular implementation of this data collection phase can be adaptedto the particular store context.)

Once the store software has collected data identifying the customer'smission, as identifying accessories for a HTC Thunderbolt phone, it thensearches a database to identify all products in its inventory that arecompatible with such device. This may be done by text-searchingdatasheets for store products, to identify those that have relatedkeywords. Or, the vendors of accessories may make such compatibilityinformation available to the store in a standardized form—such as by alisting of UPC codes, or other such identifiers for each product withwhich an accessory is compatible.

In one particular implementation, the store downloads a list ofidentifiers of compatible products to the shopper's device. The softwarethen advises the shopper to physically scan the display of protectivesmartphone cases (which is found mid-way down aisle 8B, if the shopperis not already there), and informs the shopper that the phone willdisplay a green light (or output another confirmatory signal) for thoseaccessories compatible with the HTC Thunderbolt.

The scanning mechanism can be of various sorts—again depending on thecontext. The product packages may each be equipped with an RFID or NFCchip, which serves to electronically identify the product to asmartphone when the phone is brought into close proximity. (NFC readerswill soon be standard features of most smartphones.) Or, imagerecognition techniques can be used. (Although numerous, there is alimited number of protective cases on the wall, each with differentpackaging. The store computer can download visual fingerprint data, suchas SIFT or SURF data, or other characteristic information by which thesmartphone can visually identify a particular package from this limiteduniverse, by analysis of streaming camera data.)

In still another arrangement, the smartphone applies imagery captured byits camera to a watermark detector, which extracts plural-bit dataencoded into the artwork of the product packaging. Or barcode readingcan be used.

As the phone harvests identifiers from nearby products, thepreviously-downloaded list of identifiers for compatible devices ischecked for matches. If the identifier of a scanned product is foundamong the downloaded list of compatible products, a suitable indicationis output to the user.

By such arrangement, the smartphone acts in a manner akin to a Geigercounter. As the customer moves the phone along the displayed protectivecases, it issues a signal to draw the customer's attention to particularitems of interest (i.e., those cases adapted to protect the HTCThunderbolt phone). The user can then focus her inquiry on otherconsiderations (e.g., price and aesthetics), rather than puzzling overthe basic question of which cases are suitable candidates for purchase.

It will be recognized that the foregoing arrangement is subject tonumerous variations, while still providing an interactive guide tocompatibility. For example, the store needn't download a list ofcompatible identifiers to the smartphone. Instead, the smartphone cansend sensed identifiers to the store computer, which can then match suchidentifiers against a list of compatible products. Similarly, a list ofcompatible products needn't be generated in advance. Instead, the storecomputer can receive scanned identifiers from the customer's smartphoneand then determine, on-the-fly, if the scanned product is compatible(e.g., by then-recalling and checking data associated with that productfor an indication that the HTC Thunderbolt phone is one of the productswith which it is compatible).

Likewise, the detection of product identifiers from sensed packagingneedn't be performed by the phone. For example, camera imagery may bestreamed from the phone to the store computer, where it can be processed(e.g., by pattern-, watermark- or barcode-recognition techniques) toobtain an associated identifier.

The identifier needn't be discerned/derived from the product packaging.Shelf tags or other markings can also serve as the basis for productidentification.

Depending on the particular implementation, there may be a step ofmapping or translating identifiers to determine compatibility. Forexample, a shelf tag may bear the store's proprietary SKU number.However, the reference data by which compatibility is indicated (e.g., aproduct's datasheet) may identify products by UPC code. Thus, the systemmay need to look-up the UPC code from the sensed SKU number indetermining compatibility.

Naturally, these principles can be applied to other related productpairings, such as finding a car charger for a video player, finding anobscure battery for a cell phone, or finding a memory card for a camera.

Computational Photography and Subliminal Reference Information

Computational photography refers to image processing techniques thatalgorithmically alter captured image data to yield images of enhancedform. One example is image deblurring.

Image blur is a particular problem with smartphone cameras, due to thenecessarily small size of the camera aperture, which limits the amountof light delivered to the sensor, thus requiring commensuratelylengthened exposure times. Lengthened exposure times require the user tohold the camera steady for longer periods—increasing the risk of motionblur. (The light weight of such phones also increases the risk of motionblur—they lack the inertial stability that heavier cameras, such asSLRs, offer.)

Blur can be introduced by phenomena other than motion. For example, lensoptics typically focus on subjects within a particular focal plane anddepth of field. Objects that are outside the focused field are blurred(so-called “defocus blur”).

Blur functions can be characterized mathematically and, oncecharacterized, can be counteracted by application of an inversefunction. However, blur functions cannot usually be measured directly;rather, they typically must be estimated and iteratively refined.Recovering the blur function from a blurred image (known as theblind-deconvolution problem) is an uncertain endeavor, since the blurredimage alone typically provides only a partial constraint.

To help disambiguate between alternate original images, and betterestimate the associated blur function (generally a blur “kernel”), it ishelpful to know something about the unblurred original image—a so-called“prior constraint” (or simply, an “image prior”).

For example, in published patent application 20090324126, Microsoftresearchers observe that imagery is generally characterized by regionsof similar coloration. Even if blurred somewhat, these regions tend toretain their same general coloration. Because local image color tends tobe invariant to blur, it can serve as an image prior that can be used tohelp yield a better estimate of the blur function.

Another image prior was used to help sharpen imagery from the Hubbletelescope, which originally suffered from mirror deformities thatintroduced distortion. It was understood that most light sources in thecaptured imagery were circular disks (or point sources). With thisknowledge, candidate corrective blur kernels could be iterativelyrevised until the processed imagery depicted stars in their originalcircular disk form. (See, e.g., Coggins, et al, Iterative/RecursiveDeconvolution with Application to HST Data,” ASP Conference Series, Vol.61, 1994; and Adorf, “Hubble Space Telescope Image Restoration in itsFourth Year,” Inverse Problems, Vol. 11, 639, 1995.)

(Another group of deblurring techniques does not focus on priorinformation about features of the captured image, but rather concernstechnical attributes about the image capture. For example, theearlier-referenced research team at Microsoft equipped cameras withinertial sensors (e.g., accelerometers and gyroscopes) to collect dataabout camera movement during image exposure. This movement data was thenused in estimating a corrective blur kernel. See Joshi et al, “ImageDeblurring Using Inertial Measurement Sensors,” SIGGRAPH '10, Vol 29, No4, July 2010. (A corresponding patent application is also believed tohave been filed, prior to SIGGRAPH.) Although detailed in the context ofan SLR with add-on hardware sensors, applicant believes the Microsoftmethod is suitable for use with smartphones (which increasingly areequipped with 3D accelerometers and gyroscopes; c.f. the Apple iPhone4).)

In accordance with another aspect of the present technology, knownreference information is introduced into scenes that may be imaged bycameras (e.g., smartphones), to provide image priors that allow imageenhancement.

Consider the cereal box depicted in FIG. 12. Its artwork is subliminallyencoded with marker features that are too high in spatial frequency tobe visible to human observers. Yet the form and frequency of thesemarkers are known in advance. (They are typically standardized, inaccordance with common watermarking techniques. An example is theDigimarc image watermarking technology, which is provided with AdobePhotoshop.) These markers can thus be used as image priors—allowingimagery of the cereal box to be processed to counteract any motion- ordefocus-blur.

The prior information can be used in the spatial-frequency domain (whereit appears as pentagonal constellations of impulse functions), or in thepixel domain (where it appears as a characteristic weave pattern—toohigh in frequency to be discerned by human viewers but detectable fromcamera-captured imagery).

Using known blind deconvolution techniques, such priors allow iterativerefinement of a blur kernel, which can then be applied to counteract anyblur in the captured imagery.

An exemplary implementation uses the Richardson-Lucy technique, whichdates back to two publications: Richardson, “Bayesian-Based IterativeMethod of Image Restoration,” J. Opt. Soc. Am. 62, 55-59, 1972; andLucy, “An Iterative Technique for the Rectification of ObservedDistributions,” The Astronomical Journal, Vol. 79, No. 6, June, 1974.Such methodology has been refined in succeeding decades. Examples areshown in patent applications by Microsoft and MIT, e.g., 2010123807,2008240607, 2008025627, 2010074552, and 2009244300.

Most blurring in the pixel domain is manifested more as a reduction inintensity at high frequencies in the Fourier domain—rather than as ablurring in the frequency domain. Hence, the ability to find a sharplydefined pattern in the Fourier domain tends to withstand pixel domainblurs—provided the amplitude of the Fourier domain signal is sufficient.The particular amplitude in a particular application can be determinedheuristically. If correction of only slight blurs is anticipated (e.g.,motion blurs due to small hand jitter in a smartphone cameraapplication), then relatively low amplitude Fourier marker signals canbe employed. If more substantial blurring is expected, then strongermarker signals should be used. (The diminution in amplitude can bemitigated by putting the marker(s) relatively lower in frequency, e.g.,closer to line 217 in FIG. 11.)

As just-noted, marker signals may be tailored in frequency to optimizetheir utility with respect to blur compensation. They may also betailored in form. For example, instead of markers composed of fiveimpulse functions—as in FIG. 11, a blur-redressing marker signal maycomprise a lesser number of elements, such as one or two. Similarly,instead of impulse function components, such markers may be comprised ofelongated segments, arranged horizontally, vertically, and/or atintermediate angles—to help improve robustness in the presence of motionblur. An example is the pattern 302 shown in FIG. 14.

As detailed in U.S. Pat. No. 6,590,996, a watermark signal can includevarious sets of signal elements. One set can comprise a set ofregistration signals. These are encoded relatively strongly, and enablethe translation, scale and rotation of the watermarked imagery to bedetermined. Once these parameters are known, a thus-informed watermarkdetector can then recover a second set of elements, which are morenumerous (and are typically more weakly encoded), that convey most (orall) of the watermark payload data.

The marker signals of FIGS. 11 and 14 can be used in a manner like theregistration signals of U.S. Pat. No. 6,590,996, to determine affineparameters about the captured imagery. And they also can serve the dualpurpose of providing image priors, for blur correction.

In a particular embodiment, blind deconvolution is applied to a blurredimage, using the subliminal markers provided by patterns 210/302 asimage priors. Iterative correction is applied to the image to reduce theblur effect—seeking to restore the image to a sharper form. (Assessingthe intensity of the blur-corrected Fourier domain marker signals is onemetric that can be used.) A watermark reading operation is thenperformed on the blur-compensated imagery—allowing recovery of theplural-bit payload information. Thus, a virtuous cycle results—themarker signals are useful in deblurring the image, and the resultingdeblurred image yields better decoded-watermark results.

In some embodiments, the watermark payload can include various bits thatconvey statistics about the original imagery. A great variety of imagestatistics have been used in the prior art as image priors to aid inremoving blur. A problem with the prior art, however, is obtainingreliable image statistics—when only a blurred image is available. Adigital watermark can provide a channel by which such information can bereliably conveyed, from the image to the deblurring system.

In some embodiments, the marker signals 210/302 can themselves conveyinformation. For example, the phases of the component marker elementscan be selectively inverted to convey a limited number of bits. Oneimage statistic that can be conveyed in this manner is average luminanceof the original artwork. This statistic offers a constraint that isuseful in assessing the accuracy of different iterated blur solutions.

(Different watermark payloads can be encoded in differentregions—commonly rectangular tiles—of the artwork. This allows severallocal statistics to be conveyed. For example, the cereal box artworkdepicted in FIG. 12 may comprise an array of 6×4 watermark tiles,allowing statistics for 24 different spatial regions to be conveyed.)

Most images do not include cereal boxes. But watermarked data can beinserted into many common environments, and serve to provide imagepriors, as described above. For example, carpet and upholstery fabriccan include watermark patterns. In any environment in which such awatermark pattern is found, the quality of imagery captured in suchenvironment can be enhanced by the foregoing blur correction techniques.(Other computation photography methods can similarly rely on suchwatermark signals.)

While most embodiments use watermark signals that are outside the rangeof human visual perception due to their frequency (e.g., outside circle217 in FIGS. 11 and 14), in other embodiments a watermark signal may beadded that escapes attention because of its chrominance. The human eye,for example, is relatively insensitive to yellow. Thus, known markerpatterns may be inserted at lower frequencies, if printed in yellow.Likewise, other inks that are generally outside the realm of humanperception, but detectable by image sensors, can also be used.

Looking ahead, online photo repositories such as Flickr and Facebook mayroutinely check uploaded imagery for watermarks. Whenever watermarks arefound, the service can employ such signals in computational photographymethods to enhance the imagery.

(While described in the context of a post hoc image correctionprocedure, the same techniques can similarly be employed before orduring the image capture process. For example, subliminal marker signalscan aid a camera's auto-focus system in determining where focus shouldbe established.)

More on Blur

The cited Joshi et al paper teaches how inertial data can be used torefine an estimate of a blur kernel. But a simpler application ofinertial data may ultimately be more widely useful.

In one particular arrangement, a smartphone camera captures a sequenceof image frames (e.g., in a streaming capture-, or video-mode). Duringeach frame, motion of the phone is sensed—such as by the phone's 3Dgyroscope and/or accelerometer. Selected ones of the stream of imageframes (i.e., selected based on low phone motion) are then aligned andcombined, and output as an enhanced image.

Such an enhanced image can be applied, e.g., to a digital watermarkdetector. The image enhancement allows the detector to output thedecoded information more quickly (since it needn't work as long inrecovering marginal signals), and allows for more robust watermarkrecovery (e.g., decoding despite poor illumination, image corruption,and other challenges).

The selection of image frames that are to be combined can proceed indifferent fashions. For example, a motion threshold can be set (e.g., ingyroscope-sensed degrees of rotation per second of time), and frameshaving motion below that threshold can be combined. (Or, in anotherview, frames having motion above that threshold are disregarded.) Thenumber of frames to be combined can be set in advance (e.g., use thefirst six frames that meet the threshold criterion), or the techniquecan utilize all frames in the sequence that pass such test. Anotheroption is to set a threshold in terms of target frame count (e.g., ten),and then select—from the captured sequence of frames—the target numberof frames that have the lowest values of motion data (of whatevervalue).

The combination of frames can be by simple averaging. Or, weightedaveraging can be used. The weight assigned to each frame can depend onthe associated motion data. Desirably, the weighting is moreparticularly based on relationships between the frames' respectivemotion data, so that the “stiller” a frame, the more it contributes tothe average. Preferably, if one or more frames have zero motion, theyshould be given a maximum weight value, and frames with non-zero motionvalues should be given a zero weight value. One algorithm forestablishing such a frame-dependent weighting factor “k” is:

k _(A)=[Motion(Frame_(MIN))/Motion(Frame_(A))]^(X)

where k_(A) is the weighting factor for Frame “A;” Motion(Frame_(A)) isthe motion, in degrees per second, of frame “A”; Motion(Frame_(MIN)) isthe minimum motion among all of the frames in the selected set, and X isan exponential ratio-ing factor.

In addition to reducing blur, such techniques are also effective forde-noising smartphone-captured imagery.

Hybrid Watermark/Salient Point/Barcode/NFC Arrangements

Earlier-cited application Ser. No. 13/079,327 details an arrangement inwhich imagery captured from a printed document (e.g., a newspaper) isrendered on a smartphone screen in conjunction with auxiliaryinformation, which is overlaid in geometrically-registered fashion.Published application 20080300011 details related technology.

The preferred embodiments of these just-noted applications discern thepose of the smartphone relative to the page by reference to registrationsignal components of a watermark signal encoded in the page. The payloadof this watermark is used to access a database containing auxiliaryinformation related to the page. This auxiliary information is thenoverlaid on top of the imagery captured from the page, at a position onthe screen that is dependent on the discerned pose.

Earlier-cited application Ser. No. 13/011,618 teaches a somewhatdifferent arrangement, in which the user taps on a portion of an imagedpage presented on the smartphone screen. A watermark payload decodedfrom the captured imagery is sent to a database, which returns pagelayout information corresponding to the page being viewed. (The pagelayout data was earlier exported from publishing software used whencomposing the page, and stored in the database.) By reference to scaleand rotation information discerned from registration signal componentsof the watermark, in conjunction with the retrieved page layout data,the phone determines the coordinates on the physical page indicated bythe user's tap (e.g., 4 inches down, and 6 inches to the right, of theupper left corner of the printed page). By reference to these determinedpage coordinates, auxiliary information relating to that particularportion of the page is identified, and presented on the smartphonescreen.

In accordance with another aspect of the present technology, differentarrangements for presenting information corresponding to differentlocations on a printed page—or other object—are utilized.

In one particular embodiment, location of the smartphone relative to thepage is not determined by reference to registration components of thewatermark signal. Instead, the decoded watermark payload is sent to aremote server (database), which returns information about the page.Unlike application Ser. No. 13/011,618, however, the returnedinformation is not page layout data exported from the publishingsoftware. Instead, the database returns earlier-stored reference dataabout salient points (features) that are present on the page.

(The salient points may be identified simply in terms of theircoordinates on the original page, e.g., by inches down and across from atop corner of the page. Additionally or alternatively, otherinformation—typically feature vectors—can be provided. Instead ofidentifying individual, unrelated points, the information returned fromthe database may characterize a constellation of salient points.)

The smartphone can use this knowledge about reference salient points onthe page being viewed in various ways. For example, it can identifywhich particular part of the page is being imaged, by matching salientpoints identified by the database with salient points found within thephone's field of view.

The auxiliary data presented to the user can also be a function of thesalient points. For example, the smartphone can transmit to a remoteserver a list of the identified salient points that are matched withinthe phone's field of view. Since this subset serves to preciselylocalize the region of the page being viewed, auxiliary informationcorresponding particularly to that region (e.g., corresponding to aparticular article of interest to the user) can be returned to thephone. Alternatively, a larger set of auxiliary data, e.g.,corresponding to the entirety of the page, or to all pages in thenewspaper, can be returned from the database in response to thewatermark payload. The smartphone can then select from among this largerset of data, and present only a subset that corresponds to theparticular page excerpt being imaged (as determined by salient points).As the user moves the phone to image different parts of the object,different subsets can quickly be presented.

Another way that reference salient points returned by the database canbe utilized is in determining the phone's pose relative to the page. The3D pose of the camera relative to the object, together with theprojection of that view through the camera lens, uniquely determineswhere the salient points appear in the captured image. Given thecaptured image, and reference data about position of the salient pointsin a plan view of the object, the 3D pose can be determined. (Accuratedetermination of pose requires some information about the projectioneffected by the camera/lens, e.g., the focal length and image format.)

Once the object pose is determined, any overlaid information can begeometrically registered with the underlying imagery, e.g., with arotation, scale, translation, and/or affine- or perspective-warp thatmatches the smartphone's view of the page.

(Overlaying information on an image in geometrically-registered fashion,based on salient points, is known from augmented reality. See, e.g.,U.S. Pat. Nos. 7,616,807, 7,359,526, 20030012410 and 20100232727, andthe articles: Reitmayr, “Going Out: Robust Model-based Tracking forOutdoor Augmented Reality,” Proc. 5^(th) IEEE/ACM Int. Symp. on Mixedand Augmented Reality, 2006, pp. 109-118; and Genc, “Marker-lessTracking for AR: A Learning-Based Approach, Proc. 1st IEEE/ACM Int.Symp. on Mixed and Augmented Reality, 2002, pp. 295-304.)

In one particular arrangement, the database also returns scale androtation data, related to salient point information provided to thesmartphone. For example, the database may return a numeric value usefulto indicate which direction is towards the top of the imaged object(i.e., vertical). This value can express, e.g., the angle betweenvertical, and a line between the first- and last-listed salient points.Similarly, the database may return a numeric value indicating thedistance—in inches—between the first- and last-listed salient points, inthe scale with which the object (e.g., newspaper) was originallyprinted. (These simple illustrations are exemplary only, but serve toillustrate the concepts.)

Relatedly, the salient points returned from the database can also serveas guides in sizing and positioning graphical indicia—such as boxes,borders, menus, etc. For example, the smartphone may be instructed torender a bounding box on the phone display—sized just large enough toencompass salient points numbered 5, 32, 44 and 65, with edges parallelto the display edges. The salient points can similarly serve asin-object guideposts by reference to which other information can besized, or presented.

Still another use of reference salient point information is indetermining intrinsic parameters of the camera's lens system, such asfocal length. Typically, such specs are available from the manufacturer,or are available in metadata output by the camera (e.g., in EXIF data).However, if unknown, lens parameters can be determined empirically fromanalysis of images containing known salient points, as is familiar toartisans in the field of photogrammetry. (Others may consult referenceworks, such as the book by Hartley, “Multiple View Geometry in ComputerVision,” Cambridge University Press, 2004, and the thesis by Pollefeys,“Self-Calibration and Metric 3D Reconstruction from Uncalibrated ImageSequences,” Catholic University of Leuven, 1999, in implementing suchmethods.)

In the arrangements described above, the registration components of thewatermark signal are not be used; only the payload of the watermark isemployed. In such arrangements, other data-conveying mechanisms mayalternatively be used, such as barcodes, OCR, Near Field Communicationchips (RFIDs), etc.

Consider a printed poster that includes an embedded or attached NFCchip. A smartphone, equipped with a NFC reader, senses a plural-symbolidentifier from the NFC chip of the poster—which serves to identify theposter. This poster-identifying information is transmitted by the phoneto a database, which returns salient points associated with the poster.The user can then interact with the poster in a position-dependentmanner.

For example, instead of presenting response data that is generic to theposter as a whole (i.e., the typical NFC usage model), a user can imagedifferent areas of the poster with the smartphone camera. The phoneidentifies salient points in the captured imagery, and matches them withsalient points returned from the database in response to submission ofthe NFC poster-identifying data. By such arrangement, the smartphonediscerns what excerpt of the poster is being imaged (and, if desired,the phone's pose relative to the poster). Auxiliary informationparticularly corresponding to such excerpt is then presented to the user(as a geometrically-registered screen overlay, if desired). Thus, such auser can be presented one response if viewing a first part of theposter, and a different response if viewing a second part of the poster.

More generally, such salient point methods can serve as highly accuratelocation determination methods—much finer in resolution than, e.g., GPS.Consider a venue that includes a poster. The position of a fixed pointon the poster (e.g., its center) is determined in advance, and suchinformation is stored in a database record identified by the payload ofan NFC chip included in the poster (or is encoded as part of the chip'sdata payload). A user sensing this NFC chip obtains the locationcoordinates of the poster, as well as salient point information relatingto the poster artwork, from the database. The smartphone then analyzesimagery captured from the phone's current viewpoint, and discerns thephone's pose relative to the poster (e.g., three inches to right ofcenter, four inches down, and 24 inches from the poster, viewing upwardat an inclination of ten degrees, rightward at an angle of 20 degrees,with the phone inclined four degrees clockwise to the poster). By usingthis salient point-determined pose information, in conjunction with theknown position of the poster, the phone's absolute 6D pose isdetermined.

Naturally, such methods can be used with objects other than posters. Andthe thus-determined smartphone location can be used in connection withmost methods that rely on a location determination.

Salient points—sometimes known as interest points, or local features—arefamiliar from content-based image retrieval (CBIR) and other image-basedtechnologies. Generally speaking, such points are locations in an imagewhere there is a significant local variation with respect to one or morechosen image features—making such locations distinctive and susceptibleto detection. Such features can be based on simple parameters such asluminance, color, texture, etc., or on more complex metrics (e.g.,difference of Gaussians). Each salient point can be represented by dataindicating its location within the image, the orientation of the point,and/or a feature vector representing information associated with thatlocation. (A feature vector commonly used in SURF implementationscomprises 64 data, detailing four values of luminance gradientinformation for each of 16 different square pixel blocks arrayed aroundthe interest point.)

Salient points may correspond to individual pixels (or sub-pixellocations within an image), but salient point detectors typically focuson 2D structures, such as corners, or consider gradients within squareareas of pixels. Salient points are one particular type of local imagedescriptors. The arrangements detailed above can be used with other suchdescriptors as well. In a particular implementation, salient points usedby the SIFT or SURF algorithms can be used. That is, in response toreceipt of a watermark, NFC, or other object identifier from asmartphone, a remote server/database can return a set of SIFT or SURFdata corresponding to that object.

(SIFT is an acronym for Scale-Invariant Feature Transform, a computervision technology pioneered by David Lowe and described in various ofhis papers including “Distinctive Image Features from Scale-InvariantKeypoints,” International Journal of Computer Vision, 60, 2 (2004), pp.91-110; and “Object Recognition from Local Scale-Invariant Features,”International Conference on Computer Vision, Corfu, Greece (September1999), pp. 1150-1157, as well as in U.S. Pat. No. 6,711,293. SURF isrelated, and is detailed, e.g., in Bay et al, “SURF: Speeded Up RobustFeatures,” Eur. Conf. on Computer Vision (1), pp. 404-417, 2006; as wellas Chen et al, “Efficient Extraction of Robust Image Features on MobileDevices,” Proc. of the 6th IEEE and ACM Int. Symp. On Mixed andAugmented Reality, 2007; and Takacs et al, “Outdoors Augmented Realityon Mobile Phone Using Loxel-Based Visual Feature Organization,” ACM Int.Conf. on Multimedia Information Retrieval, October 2008.)

As a preliminary act to the operations described above, referencesalient point data for the object is determined (typically by aproprietor or publisher of the object from analysis of a file from whichthe object is printed), and this data is stored in a database inassociation with an identifier for that object (e.g., an NFC identifier,or watermark or barcode payload, etc.).

In some arrangements, the salient point data may not be determined andstored in advance. Instead, it may be developed through use, e.g., in acrowdsourced fashion. For example, a user may capture imagery from aposter, decode a watermark payload, and capture salient pointinformation. On querying a database with the watermark payload, thesmartphone may find that there is no salient point reference informationpreviously stored for that object. The smartphone may then be requestedby the database to provide the information discerned by the phone, towhich the smartphone can respond by transferring its salient pointinformation to the database for storage.

The smartphone may additionally send information relating to thephone-object pose. For example, the watermark detector in the phone mayprovide affine transform parameters characterizing the scale, rotationand translation of its object viewpoint—as determined by reference tothe registration signal components included in the watermark signal. Oran image processing algorithm executed by the phone processor maydiscern at least some aspect(s) of pose information by reference toapparent distortion of a known item depicted within the field of view(e.g., edges of a square 2D barcode). In still other arrangements, thephone may send the database the captured image data, and such poseestimation methods can be performed by a processor associated with thedatabase—rather than at the phone. Or pose data can be determinedotherwise (e.g., by acoustic echo techniques,accelerometer/gyroscope/magnetometer sensor data, radio-based location,etc.).

By reference to such pose information, a processor associated with thedatabase can process the phone-submitted salient point information,normalize it to reduce or remove pose-related distortions, and storesame as reference data for later use. (Or such normalization may beperformed by the smartphone, before providing the salient pointinformation to the database for storage.) This normalized salient pointinformation can then serve as reference information when a secondsmartphone thereafter queries the database to obtain reference salientpoint information for that object.

Similarly, data about edges of the object—sensed from the phone-capturedimagery, can be stored in the database. Preferably, such information isgeometrically related to the salient point information, so that thesalient points can serve to indicate, e.g., distances from differentedges of the object.

In other embodiments, instead of the database returning earlier-storedreference data about salient points (features) that are present on thepage, a copy of the page imagery itself can be returned—with or withoutassociated salient point data.

More information about salient point-based systems is presented in thefollowing sections. The details of embodiments described in suchsections can be incorporated into the above-described arrangements, andvice versa.

Salient Points and Watermark Detection

Watermark detection commonly proceeds by first estimating translation,rotation and scale of the watermarked object by reference toregistration signal components of the watermark (e.g., a knownconstellation of impulses in the spatial frequency domain). The capturedimagery is next processed to remove these estimated affine distortions.Finally, a watermark decoding algorithm is applied to the processedimagery.

In accordance with another aspect of the present technology, the pose ofthe imaged object relative to the camera is estimated through use ofreference salient points—as discussed above. Once the pose is estimated,corrective adjustments (e.g., affine counter-distortions) are made tothe captured imagery to reduce the pose artifacts, yielding a frame ofimagery that is normalized to a plan-like view. The watermark decodingalgorithm is then applied to the corrected imagery.

On a planar object, a very small set of salient points can suffice forsuch purpose (e.g., three points). Graphical indicia which are commonlyfound in printed materials (e.g., a recycling symbol, or company logos,or even square barcodes) are well suited for such purpose.Alternatively, the rectangular outline of a typical magazine page, oftypical dimensions, can also suffice.

Salient Points for Image De-Noising

Watermark signals are typically small in amplitude, and can be degradedby image noise—such as arises from low-light exposures. Other imageoperations similarly suffer from image noise (e.g., fingerprint-basedimage recognition). Image noise can be decreased by lengthening theexposure interval, but so-doing increases the risk of motion blur.

In accordance with a further aspect of the present technology, multipleimage frames of a scene are captured, such as by a smartphone in a videocapture mode. Each frame, independently, may have a poor signal-to-noiseratio. This signal-to-noise ratio is improved by geometrically aligningmultiple frames by reference to their common salient points, and thenaveraging the aligned frames. The composite frame thus-obtained is lowerin noise than the component frames, yet this advantage is achievedwithout the risk of motion blur. Such a composite frame can then besubmitted to a watermark detector for watermark decoding, or usedotherwise.

Such method works by identifying the salient points in each of theframes (e.g., using the SURF technique). Corresponding points are thenmatched between frames. The movement of the points between frames isused to quantify the transform by which one frame has changed to yieldthe next. These respective transforms are then reversed to align each ofthe frames to a common reference (which may be, e.g., the middle framein a sequence of five frames). The aligned frames are then averaged.

The video capture mode permits certain assumptions that facilitate rapidexecution of the method. For example, the frame-to-frame translationalmovement of salient points is small, so in searching a subject frame toidentify a salient point from a prior frame, the entire subject frameneedn't be searched. Instead, the search can be limited to a smallbounded neighborhood (e.g., 32×32 pixels) centered on the position ofthe point in the prior frame.

Similarly, because the frame-to-frame rotational transformation ofsalient points is likely to be small, the feature vectors for the pointscan omit the customary orientation information.

Likewise, the scale factor of the imagery captured in the sequence offrames is likely to be relatively uniform—again constraining the searchspace that must be considered in finding matching points.

A particular matching algorithm starts with salient pointsconventionally identified in first and second frames. An exemplary framemay have 20-400 salient points. For each point in the first frame, aEuclidean distance is computed between its feature vector, and thefeature vector of each salient point in the second frame. For each pointin the first frame, a point in the second frame with the closestEuclidean distance is identified as a candidate match.

Sometimes, a point in the second frame may be identified as a candidatematch to two or more points in the first frame. Such candidate matchesare discarded. Also discarded are candidate matches where the computedEuclidean distance exceeds a threshold. (An absolute value threshold maybe used, or the algorithm may discard the candidate matches based on thelargest ten percent of distance values.) A set of candidate matchesremains.

FIG. 20 shows the location of the remaining salient points, in both thefirst and second frames. As can be seen, points near the center of theframe closely coincide. Further away, there is some shifting—some due toslightly different scale between the two image frames (e.g., the usermoved the camera closer to the subject), and some due to translation(e.g., the user jittered the camera a bit).

To a first approximation, the transformation between the first andsecond frames is characterized by a scale factor, and by a translation(in X- and Y-). Scale is estimated first. This is done by scaling thesecond frame of remaining salient points by various amounts, and thenexamining a histogram of distances between the scaled point locations,and their nearest counterparts in the first frame. FIG. 21 shows theresults for scale factors of 1.01, 1.03, 1.05, and 1.07. As can be seen,a scale of 1.05 yields the best peak.

The second frame of remaining salient points is then scaled inaccordance with the determined scale value (1.05). Distances (in X- andY-) between the scaled point locations, and their nearest counterpartsin the first frame, are then computed, and the median values of X- andY-offset are then computed. This completes the first approximation ofthe transformation characterizing the alignment of the second imagerelative to the first.

This approximation can be further refined, if desired. One suitabletechnique is by discarding those candidate point-pairs that don't yetalign within a threshold distance after applying the determined scaleand X-, Y-offsets. An affine transform, based on the determined scaleand offsets, is then perturbed in an iterative fashion, to identify atransformation that yields the best least-squares fit between thestill-retained candidate points.

In one experiment, 500 frames of a digitally watermarked photograph werecaptured in low light using a smartphone's video capture mode.Individually, 25% of the 500 frames could be processed to read anencoded digital watermark. A successful watermark read was achieved withone or more frames in each sequence of five frames 49% of the time. Ifsuccessive groups of five frames were averaged without any alignment,the results dropped to 18%. If, however, each sequence of five frameswas aligned and averaged as described above (using the third frame as areference, against which the others were matched), a successfulwatermark read was achieved 61% of the time.

Just as the described procedure enhanced the success of watermarkreading operations, such processing of multiple image frames—based onsalient point alignment and averaging—can similarly yield low-noise,sharp, images for other purposes (including consumer enjoyment).

Another method that can be used with the foregoing arrangement, orindependently, is to note the smartphone's motion sensor datacorresponding to the instant that each frame of a video sequence wascaptured. If the sensor data (e.g., from a 3D accelerometer orgyroscope) indicates movement above a threshold value, then thecorresponding frame of imagery can be discarded, and not used in anaveraging operation. The threshold can be adaptive, e.g., by discardingtwo frames out of each sequence of ten having the highest motion values.

(Preliminary studies indicate that the magnitude of handheld motionvaries widely from instant to instant in a handheld phone—from nil, torelatively large values. By discarding the latter frames, much enhancedresults can be achieved.)

Channelized Audio Watermarks

Audio watermarks are increasingly being used to provide network servicesin association with audio and audio-video content. One example is theGrey's Anatomy Sync application offered by ABC Television, and availablefor download from the Apple App Store. This iPad app allows viewers ofthe Grey's Anatomy program to interact with other fans, and obtainepisode-related content in real-time, while watching the program.Similarly, audio watermarks in music content can allow listeners tointeract with other fans, and obtain related information.

Audio watermark information is typically woven into the contentitself—inseparable from the audio data. Removal of the watermarktypically is very difficult, or impossible.

In accordance with another aspect of the present technology, audiowatermark information is conveyed in a separate audio channel, so thatsuch information can be rendered—or not, depending on the desires of theuser, or on other circumstances.

One suitable format is Dolby TrueHD, which can convey 24 bit audio ineach of 8 discrete audio channels. Another is Microsoft's WAV fileformat, which now supports multiple audio channels. Yet another is theRF64 format, as specified by the European Broadcasting Union.

An exemplary implementation is a home audio system, using anaudiophile's 5.1 or 7.1 surround sound system, with associated watermarkdata conveyed on an additional channel. By a control on the audioreceiver (and/or on a remote control device), the user can instructwhether the watermark channel should be rendered, or not. If renderingis selected, the receiver mixes the watermark data into one or more ofthe speaker channels (e.g., the front left and right speakers). Theamplitude of the watermark is usually not changed in the mixing, butsome implementations may additionally give the user some ability to varythe amplitude of the mixed watermark.

Another implementation looks forward to the day that audio is deliveredto consumers in the native multi-track form in which it was recorded,allowing users to create their own mixes. (E.g., a consumer who is fondof saxophone may accentuate the saxophone track in a 16-track recordingof a band, and may attenuate a drum track, etc.) Again, in suchimplementation the user is given the opportunity of including thewatermark signal in the final audio mix, or leaving it out—depending onwhether or not the user plans to utilize network services or otherfeatures enabled by the watermark.

In most implementations, a single watermark track is provided. However,multiple tracks can be used. One such embodiment has a basic watermarktrack, and plural further tracks. Each of the further tracks is a datachannel, which specifies an amplitude component of the watermark thatshould be associated with a corresponding audio (instrument) track. Inthe example just given, if the drum track is attenuated in amplitude,then it may be desirable to similarly attenuate certain features of thewatermark signal that which rely on the drum track for masking. Theamplitude data channels are scaled in accordance with the user-setamplitude of the corresponding audio (instrument) track, and the scaledamplitude data from all such channels are then summed to yield a netscale factor for the watermark signal. The watermark signal is thendynamically adjusted in amplitude in accordance with this scale factor(e.g., by multiplying), so that the watermark amplitude optimallycorresponds to the amplitudes of the various audio tracks that comprisethe aggregate audio.

Disambiguation of Multiple Objects in a Captured Scene

Often a smartphone may capture an image frame that depicts severaldifferent objects in a shared context. An example is a department storeadvertisement that features a variety of products within a singlephotographic image. Another is a page of classified advertising. Suchdocuments including plural different objects may be referred to as“composite subjects.” Often it is desirable for each object that formspart of the composite subject to be associated with a differentelectronic response (e.g., a corresponding online web page, or othertriggered action).

In accordance with a further aspect of the present technology, theforegoing can be achieved by determining an identifier associated withthe composite subject, transmitting it to a data store, and receiving inreply an authored page that includes data from which a rendering of someor all of the original composite subject can be produced. This receivedpage can define different clickable (tappable) regions. The user taps ona particular object of interest shown on the rendered page, and thesmartphone responds by instituting a response associated with thatregion, using techniques known in the art (such as via familiar HTMLhypertext markup that renders an image as a hyperlink).

In such an arrangement, the image presented on the smartphone screen maynot be imagery captured by the smartphone camera. Instead, it typicallyis a page delivered to the smartphone from a remote store. However, itshows a version of the same composite subject with which the user isinteracting.

The page delivered to the smartphone may present the composite subjectat a scale larger than can be conveniently displayed on the smartphonescreen at one time. The user can employ known touchscreen gestures,including pinching, swiping, etc., to change the display magnification,and traverse the page to bring a desired excerpt into view. Once adepicted object that is the subject of the user's interest is presentedon the display at a convenient size, the user taps it to launch anassociated behavior.

Reference was made to determining an identifier associated with thecomposite subject. This can be done in various ways. In someembodiments, each object depicted in the composite subject is encodedwith its own machine readable code (e.g., a digital watermark, barcode,etc.). If any of these is decoded, and its payload is sent to the remoteserver, the system responds with the same composite page data in return(i.e., multiple input payloads all resolve to the same output page).Alternatively, the entire composite subject may be encoded with a singleidentifier (e.g., a digital watermark that spans the full compositesubject, or a single barcode on a printed page that depicts severalobjects). Again, the system can respond to transmission of such a singleidentifier by returning page data for rendering on the smartphonedisplay.

Likewise, individual objects depicted in the composite subject, or otherexcerpts of the composite subject (including such subject in itsentirety) may be recognized by image fingerprint techniques, such asSURF. Again, such identification can map to an identifier for thatsubject, which can be associated with a corresponding electronic pagefor rendering.

In still other embodiments, the smartphone may discern an identifierfrom the composite subject without use of the smartphone camera, e.g.,by detecting an identifier from an NFC or RFID chip conveyed by thecomposite subject, using a corresponding detector.

The electronic page presented on the smartphone for user interaction mayvisually correspond, to different degrees, with the physical page thatlaunched the experience. In some embodiments, the electronic page may beindistinguishable from the physical page (except, e.g., it may bepresented from a different viewpoint, such as from a plan—rather than anoblique—perspective). In other embodiments, the electronic page may bevisually similar but not identical. For example, it may be of lowerresolution, or it may present the page with a smaller color palette, orwith other stylized graphical effect, etc.

Through such arrangements, the system replicates—on a smartphonescreen—a version of a composite subject being viewed by the user, butwith clickable/tappable regions that link to corresponding resources, orthat trigger corresponding behaviors.

It will be recognized that the foregoing arrangement avoids a potentialproblem: that of a watermark or barcode detector in the smartphonedetecting multiple machine-readable indicia within a single frame ofimagery, and being unable to discern which one is of particular interestto the user. Instead, the single frame—although it may depict multipleobjects—maps to a single electronic page in response. The user can thenunambiguously indicate which object is of interest by a tap (or byalternative user interface selection).

A related problem can arise in certain implementations of streaming modedetectors (detailed above). As the user moves the smartphone camera tocapture an image of a particular object of interest, the smartphone maycapture images of many other objects that form part of the compositesubject (about which the user may have no interest), yet the smartphonemay decode machine-readable identifiers from each.

In accordance with a further aspect of the present technology, thesmartphone may disable operation of certain modules (e.g., watermark andbarcode decoders, NFC readers, etc.) when the phone's motion sensors(e.g., accelerometers, gyroscopes and/or magnetometers) indicate morethan a threshold degree of motion. For example, if the phone sensesmovement exceeding two, four or six inches per second, it may suppressoperation of such modules. (Some motion occurs just due to natural handjitter.) The phone may resume module operation when the motion dropsbelow the threshold value, or below a different threshold (e.g., oneinch per second). By such arrangement, decoding of unintendedidentifiers is suppressed.

Print-to-Web Payoffs, e.g., for Newspapers

As indicated, print media—such as newspapers and magazines—can bedigitally watermarked to embed hidden payload data. When such payloaddata is sensed by a suitable watermark detection program on a smartphone(e.g., the Digimarc Discover app), it causes the smartphone to presentan associated “payoff,” such as to display associated online content.

If a newspaper digitally watermarks a large number of its dailyphotographs, or a large number of its daily articles, it can become alogistical challenge for the publisher to specify an appropriate payofffor each photograph/article. In the crush of production schedules, somepublishers arrange for all of their watermarked images simply to linkback to the home page of the publication's online presence (e.g., thewww<dot>nytimes<dot>com web page). Alternatively, the publisher mayspecify that the payoff for a print article is simply the online versionof the same article.

Such expedients, however, provide little added value.

In accordance with a further aspect of the present technology, anewspaper article (or image) is associated with a more valuable payoff,with little or no effort.

In one particular embodiment, before the newspaper is delivered tosubscribers (but after a watermark ID has been assigned to an article),an operator types a few (e.g., 2-5) keywords that are associated withthe article (e.g., Obama, Puerto Rico; or Stanley Cup, Bruins). Thesekeywords are stored in a database at a central computer system, inassociation with the watermark payload ID with which the article isdigitally watermarked. When a viewer thereafter uses the DigimarcDiscover app to decode the watermark, and link to a payoff, the payoffis a Google (or other provider, such as Bing) search based on thekeywords entered for that article.

In practice, the Google keyword search may be used as a default, or abackstop, payoff, in case the publisher does not specify any otherpayoff. Thus, in a typical workflow, the keywords are stored inassociation with the article, and a Google search based on the storedkeywords is initially specified as the payoff for the article.Thereafter, however, the newspaper publisher, or the writer of thearticle, may change the stored data to specify a different payoff.(Sometimes an author-specified online payoff is submitted to thepublisher with the article text, in which case this author-specifiedpayoff can be used as the online payoff from the beginning.)

In another embodiment, the keywords are not entered manually, but ratherare extracted from the text of the article, e.g., as by tag cloudtechniques. One particular tag cloud ranks nouns in an article byfrequency—possibly discarding “noise words.” Co-occurrence methods canbe used to identify phrases of two words or more. The mostfrequently-occurring terms are stored as article keywords. Suchtechniques are familiar to artisans.

In yet another arrangement, the system can predict likely keywords basedon the article author. A popular sportswriter for the Oregonian commonlywrites about the Trailblazers basketball team. Paul Krugman of The NewYork Times commonly writes about the economy. Google searches based onsuch keywords can be a suitable default payoff even in the absence ofany particular information about their articles' contents.

Still another method extracts semantic information from imagery, such asby pattern matching or facial recognition. For example, known methodscan be used to identify depictions of famous people, and familiarlandmarks, in newspaper photographs. Names discerned through use of suchtechniques can be stored as keywords for such photographs.

Discerning the contents of imagery is aided if the automated system hassome knowledge of location information relating to the image. TheOregonian newspaper, for example, frequently publishes imagery includingfaces of local and state officials. Their faces may be difficult tomatch with reference facial data drawn from faces around the world.However, knowing that such imagery is being published in the Oregoniangives the recognition system a further clue that can be used to identifydepicted people/landmarks, i.e., check first for matches with facialdata associated with Oregon.

In the foregoing embodiments, automatically-generated keywords may bereviewed by an operator, who can supervise such output and revise same,if the automatically-generated keywords seem inappropriate orinadequate.

Commonly, when a watermark-based print-to-web smartphone app senses awatermark payload, and initiates a link to an online payoff, the appthat detected the watermark disappears from the screen, and is replacedby the phone's web browser. This was the case with initial releases ofthe Digimarc Discover app. In accordance with a preferred implementationof the present technology, however, HTML 5 or Adobe Flash is used torender an associated online payoff (e.g., the cited Google search) as avisual overlay atop the camera view displayed by the watermark readingapp—without leaving that app context.

In a particular implementation, a database at the central computersystem associates watermark payload IDs with details of associatedpayoffs. This database may be maintained by the newspaper publisher, orby another party. For each watermark payload ID there is an associateddatabase record, which contains the keywords for that article (orphotograph). The database record also specifies an HTML 5 template. Whenthe database is interrogated by the smartphone app, which provides adecoded watermark ID, the database pulls the HTML 5 template, insertsthe associated keywords, and returns it to the smartphone. Thesmartphone app then renders the screen display in accordance with thereturned HTML template. (Alternatively, the database may query Googlewith the keywords, and return to the smartphone a completed form of theHTML page, which already has the Google search results included.)

It may be desirable to bound the Google results that are presented tothe user. For example, it may be awkward if the online payoff from a NewYork Times article led the reader to articles in competing newspapers.Thus, in a variant embodiment, the Google search that is presented bythe smartphone app may be domain-limited, such as to the New York Timesweb site, and to non-competing domains (e.g., Wikipedia, US governmentweb sites, etc.)

When a reader links from a newspaper article (e.g., about Obama's visitto Puerto Rico) to a corresponding page of keyword-based Google searchresults, the app can monitor what search result the user thereafterpursues. In the aggregate, such user choices can lead the system tomodify the online payoff for that article to better track users'apparent interests.

For example, a Google search with the keywords “Obama” and “Puerto Rico”yields a list of results headed by news reports of his visit (publishedby The New York Times, The Nation, Al Jazeera, etc.). Lower in thesearch results, however, is a YouTube link showing Obama dancing at arally. The HTML 5 code can observe the traffic to and/or from the app,and may indicate to the database which link(s) the user pursues. Basedon the number of user who click on the dancing link, the system mayrevise the payoff so that this result appears higher in the list ofsearch results.

The database may similarly learn of viewer interest in links relating toObama drinking “cerveza” in Puerto Rico, and eating “platanos.”

All of these terms may be added as database keywords, and used as thebasis for a search. However, the results may then exclude the formerlytop-ranked news accounts. Better, in such instance, is for the databaseto run plural Google queries—one with the original keywords;” one withthose keywords and “dancing;” one with those keywords and “cerveza;” andone with those keywords and “platanos.” The remote system can thencombine the results—based on indicated user popularity of the differentsubjects, and return these modified results to smartphones thatthereafter link from the article. These later users may then see searchresults headed by the YouTube video. The order in which the links arepresented on the smartphone app can be tailored to correspond to theirapparent popularity among the newspaper's readers.

By such arrangement, the newspaper is relieved of the burden ofspecifying an online payoff for a particular article that will bepopular with its readership. Instead, an initial Google search, withsubsequent crowd-sourced feedback, allows the system to automaticallydiscern an online payoff that fulfills the demonstrated interests of theaudience.

Sometimes, user responses to one article can influence the links thatthe newspaper associates with a second, related article. Consider anOregonian edition that includes an article about the politics of Obama'svisit to Puerto Rico on page 4, and another human interest story aboutObama's visit—including the dancing—on page 5. Traffic analysis may showthat many more readers express interest in the page 5 story (bypresenting it to their Digimarc Discover app) than express interest inthe page 4 story. There may not be enough traffic from the page 4 storyto confidently change the online payoff to such story (e.g., in themanner detailed above). However, the central system may perform asemantic comparison of the page 4 article to find—by keywordsimilarity—what other article(s) in the newspaper is related. By suchprocess, the page 5 article is found to be related to the page 4article. In such event, the system can present—as a payoff option toreaders of the page 4 article—the link(s) that prove most popular withreaders of the page 5 article.

Tags Linked to Movable Objects

Further expanding certain of the features detailed above, it will berecognized that objects may be recognized in imagery (e.g., bywatermarking or fingerprinting), and the smartphone may present tags(aka icons or baubles) in association with such displayed objects. Theassociation may be “sticky.” That is, if the field of view displayed onthe smartphone screen is changed, then the displayed tags move with theapparent motion of the objects with which they are respectivelyassociated.

While such behavior provides an intuitive response, the moving tags canprove problematic if the user wishes to tap one, e.g., to trigger anassociated action. This typically requires holding the camera with atleast one hand, while simultaneously tapping a potentially moving targeton the screen.

In accordance with a further aspect of the present technology, a tagassociated with a recognized object in displayed imagery is presented ina fixed position on the screen, together with a visual indicationlinking the tag with the object to which it corresponds.

Consider a user pointing a smartphone camera at a printed catalogdepicting a woman wearing clothing for sale. If the smartphone concludesthat the catalog printing conveys a steganographic digital watermark(e.g., because a watermark calibration or orientation signal exceeding athreshold strength is found within the captured imagery), the smartphonemay be programmed to present a distinctive graphical effect on the areaof the captured imagery where a watermark seems to be found. This effectmay comprise, e.g., shimmering, chrominance or luminance oscillation,overlaid graphical features, etc. FIG. 32 shows one such arrangement.(The overlaid stars vary in brightness or position with a frequency ofseveral Hz—indicating to the user that there is more here than meets theeye. In FIG. 32, the watermark signal was detected across the displayedimagery.)

In response to such distinctive graphical effect, the user may take anaction instructing the smartphone to complete a watermark readingoperation on the captured imagery. Such action may be a touch screengesture, a shake of the phone, a touch of a physical button, a spokencommand, etc. Alternatively, the phone may operate in a mode in which itautomatically undertakes watermark reading whenever a possible watermarksignal is detected.

In the FIG. 32 arrangement, the smartphone completes a watermark readingoperation. It then transmits the decoded watermark payload to a remotedatabase station/web service. The remote database station uses thereceived watermark payload to access a database record containingassociated information, which it then transmits back to the smartphone.

In the depicted arrangement, this information returned to the smartphonecauses the phone to present a display like that shown in FIG. 33. Thatis, the smartphone spawns three tags at the bottom edge of thescreen—where they can be conveniently tapped with the user's thumb. Onetag corresponds to the blouse worn by the woman depicted in the capturedimagery; a second corresponds to the woman's shorts; and the thirdcorresponds to the woman's handbag.

In FIG. 33, there are two types of visual indications that conceptuallylink each tag with a corresponding object. One is a tether line,extending from the tag to the object. Another is an object-customizedtag.

Concerning the tether line, if the camera is moved, causing an object tomove within the screen display, the tag desirably remains fixed at thebottom edge of the screen, together with the bottom end of theassociated tether line. However, the top end of the tether line moves totrack the object, and to maintain a persistent visual associationbetween the object and the tag. This can be seen by contrasting FIGS. 33and 34. The user has moved the smartphone between these two Figures,yielding a different view of the catalog page. The woman wearing theblouse and shorts has moved rightward in the displayed field of view,and the handbag has moved out of sight. The blouse and shorts tags,however, remain stationary at the bottom of the screen. The tops of thetether lines move to track the moving blouse and shorts objects. (Thetag for the handbag, which has moved out of sight of the camera,disappears in FIG. 34.)

The depicted tether line arrangement thus employs a dual form oficonography. One follows the object (i.e., the tether line), and theother is stationary (i.e., the tag).

As indicated, the second visual indication linking each tag to arespective object is the distinctive graphical tag artwork indicatingthe nature of the object to which it corresponds. It will be recognizedthat in the depicted arrangement, the tether lines are not needed,because the distinctive tags, alone, symbolize the different object inthe image (blouse, shorts and handbag)—providing the requisite visualindication of association. But in other embodiments, the tags can begeneric and identical to each other, in which case the tether linesprovide a suitable visual association.

(Many other forms of visual indication—to associate tags with imageobjects—can be substituted for the two types shown. One other form is tohighlight, or outline, an image object in a color, and then provide atag of the same color at the edge of the smartphone display. Differentcolors can be used to associate different objects with different tags.)

Behind the scenes, in a particular embodiment, the watermark payload (orfingerprint data) sent to the web service enables access to a databaserecord containing information about the particular catalog, and page,being viewed. The information returned from the database may includereference image data characterizing particular features in the image.This information may comprise one or more thumbnail images ormaps—defining the different object shapes (blouse, shorts, handbag,etc.). Additionally, or alternatively, this information may compriseimage fingerprint data, such as data identifying features by which thedepicted object(s) may be recognized and tracked. Additionally, oralternatively, this information may comprise data defining object“handles”—locations in or at the edges of the object shapes where theupper ends of the tether lines can terminate. FIG. 35 shows one suchshape (for the shorts) that defines three handle locations (indicated by“X”s).

It will be recognized that, in this particular example, the informationreturned from the database is typically authored by a publisher of thecatalog. The publisher specifies that the FIG. 32 image includes threeobjects that should be provided with user-selectable interactivity, vialinked tags. Information about the three objects is stored in thedatabase and provided to the phone (e.g., shape data, like FIG. 35, orfingerprint data—such as SURF), allowing these objects to bepattern-matched and tracked as the view moves, and the attachment handlepoints are identified. The publisher-specified data further defines theparticular icon shapes that are to be presented at the bottom of thescreen in association with the three different objects.

When the tags are first overlaid on the captured imagery, as in FIG. 33,the software may analyze the bottom edge of the imagery to identifywhere to best place the tags. This decision can be based on evaluationof different candidate locations, such as by identifying edges withinthat region of the imagery. Desirably, the tags should not be placedover strong edges, as this placement may obscure perceptually relevantfeatures of the captured imagery. Better to place the tags overrelatively “quiet,” or uniform, parts of the image—devoid of strongedges and other perceptually salient features, where the obstructionwill likely matter less. Once a tag is initially placed, however, it isdesirably left in that location—even if the underlying captured imageryshifts—so as to ease user interaction with such tag. If the imageryshifts, the tops of the tether lines desirably follow the movingfeatures—stretching and repositioning as needed, akin to rubber bands.(In some cases, moving of tags may be required, such as when additionalobjects come into view—necessitating the presentation of additionaltags. In such instance, the software seeks to maintain the original tagsin as close to their original positions as possible, while stillaccommodating new tags. This may involve making the original tagssmaller in size.)

Tether line(s), if used, are routed using an algorithm that identifies asimple curved path from the tag to the nearest handle on thecorresponding object. Different paths, to different object handles, canbe evaluated, and a selection of a route can be based on certaincriteria (e.g., minimizing crossings of strong edges; crossing strongedges at near a 90 degree angle—if unavoidable; identifying a route thatyields a curve having a visually pleasing range—such as a curve angle ofclose to 25 degrees; identifying a route that approaches a handle on theedge of the object from outside the object, etc.) The color of a tetherline may be adapted based on the captured imagery over which it isoverlaid, so as to provide suitable contrast. As the camera's field ofview shifts, the tether line route and color may be re-evaluated, andsuch line may terminate at a different handle on a given object in somecircumstances. (This is the case, e.g., with the tether line connectingthe shorts to the corresponding icon. In FIG. 33, a handle on the rightside of the woman's shorts is employed; in FIG. 34, a handle on the leftside is used.)

If the camera field of view encompasses several distinctly-watermarkedobjects, an indication of watermark detection can be presented over eachof the objects. Such an arrangement is shown conceptually in FIG. 36,when the smartphone detects one watermark encoded in the region ofimagery encompassing the woman's clothing, and another encoded in theregion of imagery encompassing her handbag. Here, the smartphonemodifies the region of imagery depicting the woman's shorts and blouseto present one particular graphical effect (shown as an overlaid starpattern), and it modifies the region of imagery depicting the woman'shandbag to present a different graphical effect (shown as overlaidcircles).

The graphical effect presented by the smartphone when evidence of adigital watermark is detected can have any appearance—far beyond thosenoted above. In one particular arrangement, a software API may beactivated by such detection, and output the pixel coordinates of theapparent center of the watermarked region (perhaps with otherinformation, such as radius, or vertical and horizontal extent, orvector area description). The API, or other software, may fetch asoftware script that defines what graphical effect should be presentedfor this particular watermark (e.g., for this payload, or for awatermark found in this area). The script can provide effects such as amagnifying glass, bubbles, wobbles, fire animation, etc., etc.—localizedto the region where the API reports the watermark appears to be located.

In one particular embodiment, as soon as such a regional watermark isdetected, the smartphone begins to display on the screen a tether linestarting in, or at the edge of, the watermarked region, and animates theline to snake towards the edge of the screen. By the time the extendingline reaches the edge, the smartphone has sent the decoded watermark tothe remote database, and received in response information allowing it tofinalize an appropriate display response—such as presenting a handbagicon where the animated line ends. (It may also snap the upper end ofthe line to a handle point defined by the received information.)

In the case of fingerprint-based image-object identification, there istypically no shimmering or other graphical effect to alert the user ofinteractivity associated with the presented imagery. And behavior likethat described above would normally not be launched automatically, sincethere is no image feature to trigger it. (Some implementations, however,may use other triggers, such as barcodes or RFID/NFC-sensed data.)Instead, the user would initiate such action by a user instruction. Inresponse, the phone transmits the imagery (or fingerprint data basedthereon) to a remote system/web service. The remote system computes afingerprint (if not already provided by the phone), and seeks toidentify a matching reference fingerprint in a database. If a match isfound, associated information in the database serves to identify theobject/scene depicted by the imagery (e.g., a particular catalog andpage). Once such identification has been performed, the behaviordetailed above can proceed.

As described above, when the camera field of view is moved so that thehandbag is no longer shown, the corresponding handbag tag is removedfrom the bottom of the screen. In other embodiments, the user can takean action that the smartphone interprets as a command to freeze thecurrently-displayed image view, and/or maintain the presently-displayedtags on the screen. Such functionality allows the user to point thecamera at a catalog page to obtain the corresponding tags, andthereafter reposition the phone to a more convenient position forinteracting with the image/tags.

While described in the context of captured imagery, it will berecognized that these embodiments, and those elsewhere in thisspecification, can be implemented with imagery obtained otherwise, suchas received from a storage device, or from across a network.

Similarly, while object identification is performed in the detailedarrangement by watermarking (or image fingerprinting), other embodimentscan be based on other forms of identification, such as barcodes, glyphs,RFID/NFC chips, etc.

Smartphone Hardware and RDF Triples

Smartphones are increasingly being equipped with graphics processingunits (GPUs) to speed the rendering of complex screen displays, e.g.,for gaming, video, and other image-intensive applications.

GPU chips are processor chips characterized by multiple processingcores, and a limited instruction set—commonly optimized for graphics. Intypical use, each core is dedicated to a small neighborhood of pixelvalues within an image, e.g., to perform processing that applies avisual effect, such as shading, fog, affine transformation, etc. GPUsare usually also optimized to accelerate exchange of image data betweensuch processing cores and associated memory, such as RGB frame buffers.(Image data processed by GPUs is commonly expressed in three componentplanes, such as Red/Green/Blue, or YUV.)

While GPUs had their genesis in speeding graphics processing, they alsohave been applied to other uses. In the wider field of general purposeGPUs (GPGPU), such devices are now used in applications ranging fromspeech recognition to protein folding calculations. Many mainframesupercomputers rely on GPUs for massive parallelism. Increasingly, GPUvendors, such as NVIDIA, are providing software tools that allowspecified functions from a normal “C” language computer program to berun on their GPU-equipped video cards.

In accordance with another aspect of the present technology, certainembodiments repurpose smartphone hardware provided for graphics purposes(e.g., GPUs and RGB frame buffers) for use instead with RDF triples,such as for semantic reasoning.

Consider FIG. 22, which is a conceptual view of a memory used to store aframe of image data, which may have dimensions of 128×128 pixels. Eachpixel has three component color values: one red, one green, one blue. Anillustrative pixel is shown with RGB color values {43,35,216}. Thesedata correspond to a pixel having a color akin to royal blue.

This conceptual arrangement maps well to the storage of RDF triples.Instead of storing the three components of pixel representations, thismemory serves to store the three components of RDF triples—commonlycalled the Subject, the Predicate, and the Object.

The data stored in image memory locations typically comprise 8-bitvalues (i.e., one each for red, green, blue). Each value can be aninteger in the range of 0-255. When repurposed for RDF use, the RDFcomponents are similarly expressed as integer codes in the range of0-255. An auxiliary data structure, such as a table, can map different8-bit RDF codes to associated strings, integers, or real number values.

An example follows in which semantic reasoning is applied to a set ofinput RDF data to discern unstated relationships between people. Anexample of such an input triple to which reasoning will later be appliedis:

-   -   BOB HasChild TED        This triple expresses the information that a person named Bob        has a child named Ted. “BOB” is the Subject; “HasChild” is the        Predicate, and “TED” is the Object.

To store such data in image memory, one or more tables can be used tomap different data to corresponding integer codes. For example, a firsttable may map people's names to integer codes, e.g.:

TABLE I 0 1 ALICE 2 DAVID 3 4 TED 5 6 BOB . . . . . . 252 CHUCK 253HELEN 254 . . . 255 . . .

Such a table may be dedicated to a single component of the RDF triples(e.g., the Subject data), or it can serve two or more. The data may beall of the same type (e.g., people's names), or data of different typesmay be included. Not every 8-bit code need be mapped to a correspondingdatum.

In the present example, a further table is used to associate 8-bit codeswith different Predicates involving people, e.g.:

TABLE II 0 1 2 3 HasChild 4 5 HasBrother 6 7 HasSister 8 HasGender . . .. . .

The expression “BOB HasChild TED” can thus be expressed as the triple of8-bit codes {6,3,4}. It will be recognized that the meanings of thefirst and third codes (6 and 4) are indicated by Table I, while themeaning of the second code (3) is indicated by Table II.

FIG. 23 shows the same memory arrangement as FIG. 22, but now repurposedfor RDF use. The 8-bit integer codes {6,3,4} are stored in correspondingmemory locations in the three planes—which now represent Subjects,Predicates and Objects.

One triple is particularly detailed in FIG. 23, indicating “BOB HasChildTED.” However, image memories are typically quite large, e.g., 1024×768.Even a small 128×128 pixel memory has 16,384 different data elements, socan store 16,384 triples. FIG. 24 shows a few of potentially thousandsof such triples that may be stored in the memory.

(In Table II, not all of the Predicates use individuals' names for boththeir Subjects and their Objects. For example, one of the Predicates is“HasGender.” While the Subject of this Predicate is an individual'sname, the Object of this Predicate is “Male” or “Female.” These lattertwo data may be assigned to codes 254 and 255 in Table II.)

Returning to FIG. 22, the royal blue pixel is stored in the memory at alocation that corresponds to its position of desired presentation in arendered image. Frame buffers in smartphones typically have a one-to-onemapping with pixel elements in the display. Thus, the position at whichthe royal blue data {43,35,216} is stored in memory affects where, inthe picture that will be rendered from the memory, that blue pixelappears.

When storing RDF triples, there is no inherent mapping between memoryand display that dictates where—in memory—triples should be stored. Forexample, in FIG. 23, the {6,3,4} triple can be stored in any of the16,384 locations in a 128×128 pixel memory.

Instead of processing pixel data in the image memory to achieve adesired graphics effect, RDF triple data in the FIG. 23 memory isprocessed to apply a semantic reasoning rule. In the illustrativeexample, the reasoning infers additional relationship informationbetween people.

Consider FIG. 25, which shows a small excerpt of a smartphone memory,populated with a few RDF triples. (Both the 8-bit codes, and thecorresponding text, are depicted for explanatory convenience.)

As can be seen, the RDF data asserts that Alice has a sister Helen.Moreover, the data asserts that Bob has two sisters (Mary and Sue), achild (Ted), and two brothers (Chuck and John).

In this example, the GPU is programmed to apply rule-based reasoning todiscern a new type of relationship between individuals—that of being anuncle. Such an inferencing rule, stated in plain English, may be: if aperson has both a child and a brother, then the brother is the child'suncle. Broken down in Boolean pseudo-code fashion, the rule may beexpressed as:

IF (PERSON1 HasChild PERSON2), AND IF (PERSON1 HasBrother PERSON3), THENPERSON2 HasUncle PERSON3.

If the GPU applies this reasoning rule to the data depicted in FIG. 25,it will conclude by making two new semantic assertions:

-   -   1. TED HasUncle CHUCK    -   2. TED HasUncle JOHN

These two new assertions may be added to the FIG. 25 RDF data store.

As noted above, there is no inherent mapping between memory and displaythat dictates where particular triples should be stored. However, inthis example, applicant prefers to group similar Subjects together. Inparticular, the memory is conceptually divided into 3×3 blocks (302,304, etc.)—each devoted to a different RDF Subject. This is shown by thedark lines in FIG. 25. Up to nine different triple assertions about eachRDF Subject can be stored in such a 3×3 block.

Organizing data with such spatial locality provides an advantage—themultiple cores of the GPU—and the bus arrangements that provide eachcore with input and output—are commonly optimized to work on adjoiningneighborhoods of pixels. By enforcing spatial locality on the data, allthe assertions relating to a particular Subject can be processed by thesame GPU core. This speeds processing, e.g., because data usuallyneedn't be shared between cores.

The software that executes the reasoning can be implemented differentways. Assuming that each core works on a domain of nine triples, oneimplementation works as follows:

Check each of the nine Predicates to see if its code = 5; if so,increment counter “i″ If i=0, End Check each of the remaining Predicatesto see if its code = 3; if so, increment counter “j” If j=0, End Createi*j new assertions “X HasUncle Y” using all combinations of X and Y,where X are Objects whose Predicates have code=3, and Y are Objectswhose Predicates have code=5.

In the depicted example, the GPU's operation table is loaded withinstructions to execute the above procedure. When GPU operation is theninvoked, the device finds i=1 and j=2, and creates two new assertions,as identified above.

The foregoing procedure can sometimes be shortened by imposing a furtherspatial constraint on the storage of triples in the memory. Namely, inaddition to grouping triples with the same Subject together in a commonblock, the triples are also ordered within the block based on theirPredicate codes. Such sorting often allows the nine predicates to bechecked for a particular code without an exhaustive search.

For example, in FIG. 25, the Predicates are listed in descending order,starting with the upper left cell of each block. In the above-detailedprocedure, when checking Predicates for the code “5,” the check can stopwhen a code less than “5” is encountered. The fifth Predicate checked inblock 304 of FIG. 25 (i.e., the center cell in the 3×3 block) has thecode “3.” At this point the checking for “5” can stop—there will be nomore.

Likewise, when checking for the code “3,” the checking can begin wherethe checking for “5” stopped—since a “3” can't occur earlier in theorder. Similarly, the checking for a “3” can stop when the firstPredicate less than “3” is found. (Empty cells store a value of “0,”which is not shown for clarity of illustration.)

By sorting the triples in a block by Predicate in this fashion, it isnot necessary to check nine Predicates, twice, to count the number of“5” and “3” codes. Instead, five are checked to tally all the “5”s(i.e., stopping when the first “3” is encountered), and two more arechecked to tally all the “3”s (i.e., starting at the center cell, andstopping when the next cell—a “0” Predicate, is encountered).

A different implementation is based on template matching. Consider FIGS.26 and 27. FIG. 26 shows a subset of the templates involved. In thesetemplates, a blank box indicates “don't care.” (FIG. 27 simply givesletter names to each of the triples in the block, to ease reference whendiscussing the templates of FIG. 26.)

The GPU core checks the 3×3 Predicate plane of a block in the triplememory (e.g., 304) against each of the templates, to identify matchingcode patterns. For each match, a new “HasUncle” assertion is generated.

For example, in applying the top left template of FIG. 26, the corechecks whether triple “a” has Predicate=3 AND triple “c” hasPredicate=5. If so, a new “HasUncle” triple is created, with the Objectof input triple “a” as its Subject, and with the Object of input triple“c” as its Object.

Similarly, the GPU core applies the second template of FIG. 26 to checkwhether triple “b” has Predicate=3 AND triple “c” has Predicate=5. Ifso, a new “HasUncle” triple is created, with the Object of input triple“b” as its Subject, and with the Object of input triple “c” as itsObject. Etc.

Although there are 64 templates to match against, such comparisons arequickly done by GPU cores. And since a hundred or more different blocksof triple data may be processed in parallel by the different GPU cores,high throughput is nonetheless achieved.

Applied to block 304 of FIG. 25, it will be recognized that the twobolded templates of FIG. 26 match patterns in the depicted data,yielding the two above-identified new assertions.

Just as sorting the triples in the FIG. 25 can aid the first-definedprocedure, it can similarly aid the template-based procedure. Inparticular, the number of required templates can be halved by sortingthe triples by Predicate.

More particularly, it will be recognized that—if sorted in descendingorder by Predicate (as shown in FIG. 25), the arrangement depicted inthe top left template of FIG. 26 cannot occur. That is, there will neverbe a “3” in the top left corner of a block, and a “5” to its right. Inlike fashion, the second template is not needed, for the same reason.Indeed, half of the possible templates (i.e., those that place the “3”before the “5”) are not needed.

Some implementations of the present technology make use of numbertheory, to help or speed reasoning.

In the examples given above, a number theory procedure may be appliedfirst—as a check to determine whether there is any “HasUncle” assertionto be discerned from input data in a block. Only if this preliminarycheck is affirmative is the template matching procedure, or another suchprocedure, invoked.

An exemplary number theory that can be used in this case involves primefactors. It will be recognized that the “HasChild” and “HasBrother”predicates are both assigned prime integer codes (i.e., 3 and 5). If allof the non-zero predicate codes in a 3×3 block are multiplied together(or if all nine predicate codes are multiplied together, with “1”ssubstituted for “0”s), the resulting product will always be a multipleof 15 if the block contains at least one “3” and at least one “5.”

The GPU core performs this calculation—multiplying together thePredicate codes within the block. The result is then divided by 15(either by the GPU core, or otherwise). If there is a remainder (i.e.,if the product is not evenly divisible by 15), then the block does nothave both a 3 and a 5. It therefore cannot generate any “HasUncle”assertions, and the template-matching procedure (or other suchprocedure) can be skipped as moot.

The same multiplication product can also be used to screen for presenceof one or more “HasAunt” relationships within a block of input data. Therule for “HasAunt” is similar to that for “HasUncle,” but uses“HasSister” instead. In plain English, if a person has both a child anda sister, then the sister is the child's aunt.

In Table II, the “HasSister” Predicate is assigned a (prime) code of 7.If there is any “HasAunt” relationship in a block of input triples, theproduct of its Predicates will always be evenly divisible by 21 (i.e.,3*7).

There are 54 different primes among the integers 0-255. If these primecodes are assigned to Predicates that may be ANDed together by semanticreasoning rules (with such assignment perhaps skipping other values, asin Table II), then the presence (or co-presence) of any group of themwithin a block of Predicate data can be determined by checking whetherthe product of all nine Predicate codes is evenly divisible by theproduct of the group of primes. (E.g., to check for the co-occurrence of2, 3 and 11, check for divisibility by 66.)

The GPU may not have one core for each 3×3 block triple data. Forexample, the memory may have 1000 3×3 blocks of triple data, while theGPU may have only 200 cores. There are many ways this can be dealt with.One is for the GPU to apply the prime-screening procedure to the first200 blocks, and to copy blocks found to have “HasUncle” relations to aframe buffer. The process repeats for the second, third, fourth, andfifth 200 blocks, with copies of blocks determined to have “HasUncle”relations being added to the frame buffer. Finally, the earlier-detailedpattern matching (or another procedure) is run on the blocks in theframe buffer (all of which are known to have latent “HasUncle”relations), to generate the new assertions.

Product-of-primes is one type of number theory that can be applied.There are many others. Another class involves additive number theory.Consider the following table of predicate codes, for a simple example:

TABLE III 0 1 HasChild 2-9 <reserved> 10  HasBrother 11-99 <reserved>100  HasSister 101-255 <reserved>

This table is sparse; most 8-bit codes are reserved from assignment inorder to yield desired number theory results when Predicates arecombined. In fact, the only codes in this table are 1, 10 and 100.

This assignment of Predicate values enables another check of whether oneor more “HasUncle” relationships may be reasoned from a given block oftriples. In this particular implementation, the nine Predicate codes ina block are summed. (Again, a “0” is used for any empty cells.) Thisparticular sparse assignment of integers is designed so that, if thereis at least one “HasChild” Predicate, and at least one “HasBrother”Predicate, each of the last two decimal digits of the sum will benon-zero. A GPU core performs this check and, if it is met, the GPU canthen further process the block to extract the new “HasUncle”assertion(s), such as with one of the above-described procedures.

(A variant of this additive procedure can also check for one or more“HasAunt” relationship. In this check, a value of 100 is firstsubtracted from the sum. The core then checks that (1) the result ispositive; and (2) the last digit of the result is non-zero. If theseconditions are met, then one or more “HasAunt” relationships can beasserted from the data.)

The foregoing number theory examples are simple and a bit contrived, butdemonstrate underlying principles. Actual implementations will usuallybe different. (The particular operations involved are usually selectedfrom the basic instruction set of the GPU core(s) being used.)

While the prior example checked for non-zero decimal digits, manyapplications will instead apply number theory principles to binary orhexadecimal representations.

Implementations will often differ in other ways from the examplesillustrated. For example, the reasoning rules may involve more than twoPredicates. The number of triples in each block may be different thannine. Indeed, uniform block organization of memory is not required; someimplementations may have blocks of varying sizes, or dispense with blockorganization altogether. Sometimes the GPU cores may access overlappingareas of memory (e.g., overlapping blocks). Each plane of the triplememory may have a bit-depth other than 8 (e.g., 16). Where spatiallocality in memory is employed, the data may be grouped by identity ofPredicate or Object, rather than identity of Subject. Similarly,depending on the application, it may be desirable to sort by Subject,Predicate, or Object—either within each block, or across the entiretriple memory. Naturally, the selection of particular 8-bit codes toassign to different Predicates (or Subjects or Objects) will oftendepend on the particular context.

Just as the “HasUncle” assertions that are output by the above-detailedreasoning operations can be added to the triple database (e.g., FIG.25), so can outputs from inverse operations. For example, the inverse of“HasChild” is “HasParent.” So the triple “BOB HasChild TED” can beprocessed to yield the new, inverse, triple “TED HasParent BOB.”Similarly, the results of reasoning operations can often be inverted toprovide still richer expressions of relationships. For example, theoutput generated above, “TED HasUncle CHUCK” can be inverted to yield“CHUCK HasNephew TED.”

Inverse relationships can, themselves, be expressed as triples in theFIG. 25 memory or elsewhere, e.g., “HasUncle HasInverse HasNephew.”

Consider a different example, in which the triple store containsinformation about vehicles for sale. This information may have beenautomatically downloaded to a smartphone's memory in response to a usercapturing an image of a vehicle section of a classified advertisingpublication, using a smartphone camera (see, e.g., application Ser. No.13/079,327).

In this example, the 8-bit Subject codes may correspond to text stringsidentifying different vehicles, e.g.:

TABLE IV 0 1 1990 Honda CRX 2 2005 Ford Ranger 3 1985 Winnebago Chieftan4 2007 Toyota Sienna 5 2007 Toyota Tacoma 6 22′ car hauling trailer 72007 Ducati ST3 . . . . . .

This Table IV may be regarded as the main Subject table.

Associated with each of these Subjects will typically be multiplePredicates and Objects. The 8-bit Predicate codes, and their associatedmeanings, may be:

TABLE V 0 1 HasExteriorColor 2 HasPassengerCapacity 3HasGrossVehicleWeight 4 HasPrice 5 HasSellerPhone 6 HasEngineSize 7HasLinkForMoreInfo 8 HasTowingCapacity 9 HasDoors 10 HasUpholsteryColor11 HasEngineType 12 HasVehicleType 13 HasModelYear 14 HasMfr . . . . . .

Table V may be regarded as the main Predicate Table. (The Predicates inthis table are chosen for purposes of illustration. Many implementationswill employ standardized vocabularies, such as those of established OWLontologies.)

The 8-bit Subject codes, and their associated meanings may be:

TABLE VI 0 <Check aux table> 1 0 2 1 3 2 4 3 5 4 6 5 7 6 . . . . . . 1615  17 White 18 Blue 19 Tan . . . . . . 35   <100 lbs 36 100-200 lbs 37200-400 lbs . . . . . . 58 2-3 tons 59 3-4 tons 60 4-5 tons 61 5-6 tons62 6-7 tons . . . . . . 110 Trailer 111 Motorcycle 112 Sedan 113 StationWagon 114 SUV 115 Truck 116 RV 117 Family Van . . . 153 Gasoline 154Diesel 155 Hybrid . . . . . . 188 2011   189 2010   190 2009   . . . . ..

Table VI may be regarded as the main Object table.

As before, triples in the smartphone memory can express assertions using8-bit codes selected from these three vocabulary tables, e.g.

1985 Winnebago Chieftan HasGrossVehicleWeight 6-7 tons, is expressed{3,3,62}; and

1985 Winnebago Chieftan HasExteriorColor White, is expressed {3,1,17}.

Note that some entries in the main Object table may be used only withone of the entries in the main Predicate table. For example, Object code62 (e.g., 6-7 tons) may be used only with the HasGrossVehicleWeightpredicate. Other entries in the Object table may be used with severalentries in the Predicate table. For example, Object codes 2-5 might beused both with the HasPassengerCapacity predicate, and with the HasDoorspredicate. (Similarly, Object codes 17-19 might be used both with theHasExteriorColor predicate, and with the HasUpholsteryColor predicate.)

Often, the number of possible Object values exceeds the 256 that can beaccommodated in the 8-bit memory plane. For example, each of 250vehicles may have both a different price and different telephone numberassociated with it, i.e., 500 different values.

For Predicates whose Objects cannot be accommodated among the 256different values that can be associated with 8-bit codes in the mainObject table, an Object code of “0” can be specified in such triples.This directs the smartphone software to consult an auxiliary datastructure (e.g., table), instead of the main Object table, forcorresponding Object information. This different structure may beidentified by the Predicate name (or its number equivalent).

For example, the triple {3,4,0} concerns the price of the Winnebago.However, the Object code “0” indicates that the price is not indicatedby a value indexed by an 8-bit code in the main Object table (i.e.,Table VI above). Instead, the “0” directs the smartphone to consultauxiliary memory table #4 (referring to Predicate value 4). Auxiliarymemory table #4 may have the prices for all the vehicles, associatedwith their corresponding Subject codes (given in parentheses for ease ofunderstanding), e.g.:

TABLE VII 0 1 (1990 Honda CRX) $1,200 2 (2005 Ford Ranger) $5,300 3(1985 Winnebago Chieftan) $4,000 4 (2007 Toyota Sienna) $15,995 5 (2007Toyota Tacoma) $24,500 6 (22′ car hauling trailer) $4,000 7 (2007 DucatiST3) $8,995 8 . . . . . . . . .

In some embodiments, such auxiliary tables may be sorted by theassociated Object values (here, price), rather than the Subject codes—tofacilitate searching.

The smartphone GPU can near-instantly filter the data stored in the mainSubject-Predicate-Object memory to identify vehicles with certainsought-for parameters (i.e., those expressed in the main Object table).For example, if the user is interested in (1) trucks, (2) that can seat4-6 passengers, these parameters can be entered using a conventionalsmartphone graphical user interface (GUI), and the results can bequickly determined.

One illustrative GUI presents drop-down menus, or scrollable selectionwheels, that are populated with literals drawn from the Predicate andObject main tables. An auxiliary GUI table may be used to facilitate thedisplay of information, e.g., to provide plain English counterparts tothe Predicates, and to indicate the particular codes by which searchescan be keyed.

FIGS. 28, 29A and 29B show an example. One or more tables 400, or otherdata structure(s), stores information used in generating GUI menus. Asequence of GUI menus, 402, 404, etc., is presented on the smartphonescreen, and enables the user to enter desired search parameters.

The illustrated GUI 402 has a first scrollable window portion 420 inwhich different menu legends from column 410 of table 410 are selectablydisplayed. As depicted, the user has scrolled to the “What are youlooking for?” option.

A second scrollable window 422 is populated with second level menuchoices that correspond to the selection shown in window portion 420, asdetermined by reference to table 400. For example, since the user hasscrolled window portion 420 to “What are you looking for?” thesmartphone responds by presenting choices such as “Car,” “Truck”“Motorcycle,” and “Other” in the second window portion 422. Theseparticular text strings are drawn from column 412 of table 400, wherethey correspond to the “What are you looking for?” top level menu. Asdepicted, the user has scrolled the window 422 to indicate “Truck.”

The GUI 402 further includes a button 424 that the user can tap to entermore search parameters. Alternatively, the user can tap a “Get Results”button 426 that presents results of a search based on the user-enteredparameter(s).

Assuming the user taps button 424, the GUI stores the valuesjust-entered by the user (i.e., “What are you looking for?” and“Truck”), or 8-bit code values associated with such values, and thenallows the user to interact with window 420, and then window 422, again.This time the user selects “What passenger capacity?” from window 420.

By referring to column 412 of table 400, the smartphone knows topopulate the second window 422 with corresponding options, such as “1,”“2,” “3,” “4,” “5” and “6” (since these labels are associated with the“What passenger capacity?” menu selection). A flag (not shown) in table400 can signal to the software that it should render a second window 422a, in which the user can specify an upper range limit, when the “Whatpassenger capacity?” menu option is selected. (The original window 422then serves as a lower range limit.) In FIG. 29B, the user has scrolledwindow 422 to “4,” and window 422 a to “6.” The user is thus interestedin trucks that can seat between 4 and 6 passengers.

The user can then request search results by tapping the “Get Results”button 426.

When the user taps button 426, the search of the triple store cancommence. (Alternatively, it may have commenced earlier, i.e., when theuser completed entry of a first search parameter (“Truck”) by tappingbutton 424. That is, the search can be conducted in a series ofsuccessive screening operations, so that when the user taps the “GetResults” button, only the final parameter needs to be searched within apreviously-determined set of interim search results.)

Table 400 indicates how the smartphone processor should search thestored data to identify vehicles meeting the user's search criteria.

For example, since the user selected “Truck” as a search condition, row432 of table 400 indicates that this corresponds to a Predicate code of12 (HasVehicleType), and an Object code of 115 (Truck). The GPU searchesthe memory for triples that meet these criteria.

One way this may be implemented is by thresholding—an operation at whichGPU cores excel. That is, the memory can be filtered to identify tripleshaving Predicates greater than 11 and less than 13. The interim resultsfrom this initial operation—which comprise all triples with theHasVehicleType Predicate—may be copied to a new frame buffer. (Ortriples not meeting this threshold text can be set to {0,0,0}—“black” inimage processing terms.)

In the example given above, multiple triples may be identified by thisstep for further processing—typically one triple for each of thevehicles, e.g., {1,12,112}—the Honda CRX; {2,12,115}—the Ford Ranger;{3,12,116}—the Winnebago; (the Toyota Tacoma); {4,12,17}—the ToyotaSienna; etc.

A second search is then conducted across these interim results (e.g., inthe frame buffer)—this time to identify triples having Object code 115(i.e., for “Truck” objects). Triples that don't have an Object code of115 can be deleted (or set to “black”).

What remains after these two search steps (in this example) are twotriples: {2,12,115} and {5,12,115}. The Subject=2 triple corresponds tothe Ford Ranger; the Subject=5 triple corresponds to the Toyota Tacoma.The “Truck” part of the search has been completed, by identification ofthese two Subject codes.

(Another way the foregoing phase of search can be implemented is bytemplate matching, with a paired set of templates—one looking for a codeof 12 in the Predicate memory plane, and one looking for a code of 115in the Object memory plane. Again, two triples are thereby identified.)

The smartphone next applies the second search criteria—passengercapacity of 4-6. The software finds, in row 434 a and 434 b of table400, that this range corresponds to a Predicate code of 2(HasPassengerCapacity), and an Object code of 5, 6 or 7. From the firstphase of the search, it also knows the Subject code must be either 2 or5. Data meeting these Subject/Predicate/Object conditions are thenidentified (e.g., by a thresholding operation), either by examining datain the main triple memory, or by operating on a subset of the data(i.e., all triples having Subject=2 or Subject=5) in a frame buffer. Asingle triple is found to meet all these criteria: {5,2,7}. This is thetriple that expresses the 6 person passenger capacity of the ToyotaTacoma truck.

From the results of this second phase of search, the smartphone knowsthe Subject code for the vehicle matching the user's query: 5. (There isone match in this example, but in other instances, there may be severalmatches.) The smartphone next prepares search result information forpresentation to the user. This result-reporting phase of operation isillustrated by reference to FIG. 30.

Knowing which Subject code(s) corresponds to the vehicle meeting theuser's queries, the smartphone now identifies all triples in the memoryhaving a Subject code of 5. Multiple triples are found—a few of whichare shown in FIG. 30 (e.g., {5,1,17}, {5,2,7}, {5,3,58}, etc.).

The main Subject, Predicate and Object tables (tables IV, V and VI,above) are consulted for the strings or other values associated with therespective Subject, Predicate and Object Codes. For example, the firsttriple, {5,1,17} indicates “2007 Toyota Tacoma HasExteriorColor White.”The second triple, {5,2,7} indicates “2007 Toyota TacomaHasPassengerCapacity 6.” The smartphone software fills a template form,which may label different data with plain English titles (e.g., “Color”instead of “HasExteriorColor”), and presents a listing (e.g., includingall available parameters from the Predicate table V, above) to the useron the smartphone screen. (The phone may use different templates basedon certain parameters, e.g., the template used for a Truck may bedifferent than that used for a Car. The templates may be obtained, asneeded, from cloud storage, or they may be resident in smartphonememory.)

As noted, some of the parameters, such as price and phone number, maynot be stored in the main Object table. These are indicated by tripleshaving an Object code of “0.” To present data from such triples, thesoftware consults auxiliary tables corresponding to the Predicates(e.g., Auxiliary table #4 provides HasPrice values). By reference tosuch auxiliary table information, the software populates the form withinformation indicating that the price of the Toyota Tacoma is $24,500,and the seller's phone number is 503-555-1234.

Some parameters may not be specified in the data downloaded with thetriples into the smartphone, but may instead be pulled from remotetriple stores, e.g., in the cloud (or from Google-like text searches).For example EPA mileage is a government statistic that is readilyavailable on-line, and can be obtained to augment the other vehicleinformation.

An exemplary screen presenting results of such a user query may includeone or more photographs (e.g., obtained from a URL indicated by theHasLinkForMoreInfo Predicate), together with text composed using thereferenced template form. Such text may read, e.g.:

-   -   “2007 TOYOTA TACOMA, $24,500, white (tan interior) with 53,000        miles. 2.7 liter gas engine, with an EPA fuel economy of 21 mpg.        This truck features seating for 6, and has a towing capacity of        3500 pounds. Call 503-555-1234.”

A single vehicle may be detailed per screen display, with additionalvehicles brought into view by a swiping motion across the touch screendisplay. More details about presenting such information is found, e.g.,in application Ser. No. 13/079,327.

In other embodiments, triple stores utilizing more than three 8-bit dataplanes can be used. (Some images are stored in 4-plane representations,e.g., Cyan/Magenta/Yellow/Black, or RGBA—where the A stands for alpha,or transparency). A fourth 8-bit data plane enables various features.

As noted, prices are ill-suited for coding by the main Object table,since there may be 256 different values that need to be coded—leaving no8-bit codes available to represent other information. However, 8-bitcodes representing 256 different prices can be stored in a sparselypopulated fourth 8-bit data plane.

FIG. 31 shows a portion of an illustrative memory that includes thethree 8-bit planes discussed earlier, together with a fourth 8-bit planededicated to storage of codes for price. This memory is virtuallyorganized into 4×4 blocks—each dedicated to a different Subject. Thedepicted excerpt details codes associated with Subject 5 (the ToyotaTacoma truck).

As can be seen, the triple with Predicate code 4 (i.e., HasPrice) has255 for its Object code. In this implementation, a code of 255 instructsthe software to refer to a further 8-bit plane for an associated code(the particular plane being indicated by the Predicate code). In thisexample, the associated code is 218.

As in the earlier examples, a table can associate different 8-bit codeswith different values. It is advantageous, in some implementations, toassign the price codes in a sorted order, e.g., with smaller codescorresponding to smaller prices. For this 8-bit Price code memory plane,a sample table may be:

TABLE VII 0 1 $400 2 $999 3 $1,200 4 $1,500 5 $1,600 . . . . . . 218$24,500 . . . . . .

By reference to FIG. 31 and this table, it can be determined that theprice associated with Subject 5 is $24,500.

An advantage of this arrangement is that it facilitates searching, sincethe techniques detailed above—exploiting the GPU's speed at processing8-bit integers from image storage—can be utilized. The user interface ofFIG. 29B can inquire “What price?” A pair of windows 422, 422 a thenpresents controls thru which the user can scroll among actual prices ofvehicles detailed in the memory—setting a lowest price and a highestprice. Thresholding, or other such GPU operation, is then applied to thecorresponding codes in the Price memory plane to quickly identifySubjects meeting the specified price criteria.

Multiple such further 8-bit code planes can be provided. (These may beswapped into a fourth image memory plane if the hardware is so-arranged,or they can be stored and accessed elsewhere.) FIG. 31 shows anothersuch code plane—dedicated to Engine size (which corresponds to Predicate6). Again storage of corresponding codes in this 8-bit plane allowssearch queries to be executed rapidly. (GPU shaders typically are sync'dwith the display screens they drive. Even the modest GPU in the iPhone 4phone refreshes its 640×960 pixel display screen at about 25 frames persecond.)

In some implementations, most—or even all—of the Predicates may havetheir own plane of 8-bit memory for storage of codes, like thosedepicted for Price and Engine Size in FIG. 31.

It may be recognized searching is facilitated by assigning Object codesto express a semantic ordering. This is clear from the foregoing exampleconcerning passenger capacity, where the different numeric values areordered in ascending fashion, with corresponding ascendency of theassociated 8-bit codes. This enables range-based searching by specifyingupper and lower codes, and performing a thresholding operation.

A similar ordering can be effected with parameters that are not purelynumeric. For example, colors may be ordered in a semantic manner, e.g.,based on corresponding wavelength maxima, and/or intensity or luminance.Thus, all of the blues (Navy Blue, Royal Blue, Sky Blue, Aquamarine,etc.) may have similar codes, and all the reds may have similar codes,with the blue and red codes being spaced apart from each other in a0-255 code space. Range-based color searching can then readily beperformed. (E.g., a user may select “Navy Blue” in window 422 of FIG.29B, and select “Aquamarine” in window 422 a, and vehicles having anycolor code between the color codes of these two range limits areidentified.)

(Another way of dealing with colors and other features is by using anRDF ontology, which groups and associates items semantically. Forexample, the myriad different car manufacturer color names can bedistilled into searchable parameters by an ontology such as:

-   -   AQUA METALLIC HasColor BLUE    -   DEEP NAVY HasColor BLUE    -   DEEP CARDINAL HasColor RED    -   CRYSTAL RED HasColor RED    -   CHARCOAL HasColor GREY    -   GRANITE HasColor GREY        The smartphone can invert these triples, and present the        resulting Subjects (e.g., BLUE, RED, etc.) in a GUI, such as in        windows 422 and 422 a of FIG. 29B. In this case, the two values        selected in windows 422 and 422 a do not define a range of        parameters, but rather define two different values that are ORed        together in the search, so that triples meeting either value are        selected.)

The foregoing examples are somewhat rudimentary, but serve to illustratethe principles involved. More elaborate semantic reasoning can naturallybe implemented. For example, if the phone captures an image ofautomotive classified advertising, the phone may query the user to learnsome facts, such as the number of miles the user drives per year. (Thisinformation may be available elsewhere, such as in a user profile storedon a networked computer, or in a database in the user's present car.) Ifthe user responds to this query by indicating that 50,000 miles is atypical annual mileage, the phone may employ semantic reasoning todiscern that per-mile vehicle operating costs are likely of importanceto the user. With this inferred information, the phone may decide torender results of user-directed vehicle searches by presenting vehicleshaving the highest fuel economy first among the search results (absentother instruction from the user).

If EPA mileage is not available for vehicles in the search results, thephone can reason using other data. For example, semantic reasoning canbe used to conclude that an engine with a 1300 cc engine likely hasbetter fuel economy than an engine with a 4.7 liter engine. Similarly,such reasoning, or a networked knowledge base, may indicate that dieselengines tend to have better fuel economy than gas engines. Again, suchknowledge can inform presentation of the search results—simply based onthe fact that the user drives 50,000 miles per year.

While the foregoing techniques are particularly described in the contextof smartphone implementations, the principles are more widelyapplicable. Moreover, while use of GPU cores is preferred, the detailedfeatures are likewise applicable in memories that are processed withother types of processors.

Synchronized Background

Augmented reality techniques are known for recognizing image features,and overlaying information such as labels. The superimposed overlay maybe geometrically registered with the image feature(s), so that as theimage features move within the field of view, the overlay moves with acorresponding motion.

Another aspect of the present technology builds on such techniques byproviding augmentation in the form of a background, rather than anoverlay.

Consider an image depicting a subject in the foreground, and asurrounding background. An exemplary image is the Beatle's Abbey Roadrecord album, depicting the four Beatles walking across a crosswalk onAbbey Road.

Using Photoshop, GIMP, or another tool, the four Beatles may beexcerpted from the image. Two images can thereby be formed—a first imagewith just the four Beatles (surrounded by a void, or a uniformcolor—such as white), and a second image with just the background (whichmay have a void (or white) where the Beatles were, or not).

The first image may be printed on a substrate, and a smartphone is usedto capture imagery of the substrate, e.g., in a video capture mode.Software in the smartphone determines the pose of the camera relative tothe first image. With this information, the software geometrically warpsthe second (background) image, so that it has a scale, and perspective,as if viewed from the same pose. The phone then composites the twoimages—the phone-captured imagery of the four Beatles, and thebackground—warped to provide the original backdrop of the image. The twoimages complement each other to present a unified image that appearslike the original album cover, as if viewed from the phone's poserelative to the substrate.

Other background images may be used, instead of the original. Thus,instead of an image of Abbey Road, the background image may depictBroadway in Times Square, New York. The excerpted Beatles—imaged fromthe printed substrate (or from an electronic display screen) may besuperimposed on the new background image—which again is warped andscaled so that it appears with the same pose as the camera relative tothe substrate. Thus, the augmentation is more akin to an underlay ratherthan the traditional augmented reality overlay.

Geometric warping and registration of the background image to match thesubstrate-camera pose can be done in various ways, such as using digitalwatermarks, salient image points, etc. If the first image has a QR codeor other barcode, such feature can itself be used to discern poseinformation. Such techniques are further detailed elsewhere in thisdisclosure.

Once the pose of the phone relative to the first image is discerned, thesecond (background) image can be modified based on changes in pose—togive a 3D effect. For example, additional background scenery may moveinto the frame if the user pans the camera. If the user tips the camerato point more downwardly, more of the street imagery can come into view(and some sky imagery recedes out of the top of the frame). As thecamera pose changes, certain features of the second image becomeoccluded—or become revealed—by changed perspective of nearer featuresdepicted in the second image. Some such embodiments employ a 3D model togenerate the background image—computing appropriate 2D views based onthe phone's viewpoint.

While the exemplary embodiment used a first image in which the subjectdepiction was surrounded by a void, or a uniform color, in otherembodiments this is not necessary. Once the identity of the subject islearned (e.g., by fingerprinting, machine readable encoding, etc.),contours of such subject can be determined by reference to a database.The camera can then stitch the second image around the firstimage—occluding portions of the first image that are outside thedatabase-defined contours of the main subject of the image (e.g., thefour Beatles).

Desirably, if a user taps on the display screen, the phone softwareprovides a response that is appropriate to the location of the tap. Ifthe user taps on John Lennon, content related to John Lennon ispresented. Such taps invoke this behavior regardless whether the tappedpart of the display depicts imagery actually captured by the phonecamera, or whether it depicts other imagery laid-in by the phone as anaugmentation. (The phone software outputs X- and Y-locations of theuser's tap, which are then mapped to a particularly location in thedisplayed imagery. Content corresponding to such location in thepresented display of imagery can then be determined by known ways, suchas by indexing a database with the tap coordinates, by decoding awatermark at that region, etc.)

Other Comments

Having described and illustrated the principles of our inventive workwith reference to illustrative examples, it will be recognized that thetechnology is not so limited.

For example, while reference has been made to smartphones, it will berecognized that this technology finds utility with all manner ofdevices—both portable and fixed. Portable music players, desktopcomputers, laptop computers, tablet computers, set-top boxes,televisions, netbooks, ultraportables, wearable computers, servers,etc., can all make use of the principles detailed herein.

Particularly contemplated smartphones include the Apple iPhone 4, andsmartphones following Google's Android specification (e.g., the VerizonDroid Eris phone, manufactured by HTC Corp., and the Motorola Droid 2phone). The term “smartphone” (or “cell phone”) should be construed toencompass all such devices, even those that are not strictly-speakingcellular, nor telephones.

(Details of the iPhone, including its touch interface, are provided inApple's published patent application 20080174570.)

The design of smartphones and other computers referenced in thisdisclosure is familiar to the artisan. In general terms, each includesone or more processors, one or more memories (e.g. RAM), storage (e.g.,a disk or flash memory), a user interface (which may include, e.g., akeypad, a TFT LCD or OLED display screen, touch or other gesturesensors, a camera or other optical sensor, a compass sensor, a 3Dmagnetometer, a 3-axis accelerometer, a 3-axis gyroscope, one or moremicrophones, etc., together with software instructions for providing agraphical user interface), interconnections between these elements(e.g., buses), and an interface for communicating with other devices(which may be wireless, such as GSM, CDMA, W-CDMA, CDMA2000, TDMA,EV-DO, HSDPA, WiFi, WiMax, or Bluetooth, and/or wired, such as throughan Ethernet local area network, a T-1 internet connection, etc).

While this specification earlier noted its relation to the assignee'sprevious patent filings, it bears repeating. These disclosures should beread in concert and construed as a whole. Applicants intend thatfeatures and implementation details in each disclosure be combined andused in conjunction with such teachings in the others. Thus, it shouldbe understood that the methods, elements and concepts disclosed in thepresent application be combined with the methods, elements and conceptsdetailed in those related applications. While some have beenparticularly detailed in the present specification, many have not—due tothe large number of permutations and combinations. However,implementation of all such combinations is straightforward to theartisan from the provided teachings.

Elements and teachings within the different embodiments disclosed in thepresent specification are also meant to be exchanged and combined.

The processes and system components detailed in this specification maybe implemented as instructions for computing devices, including generalpurpose processor instructions for a variety of programmable processors,including microprocessors (e.g., the Atom and A4), graphics processingunits (GPUs, such as the nVidia Tegra APX 2600), and digital signalprocessors (e.g., the Texas Instruments TMS320 series devices), etc.These instructions may be implemented as software, firmware, etc. Theseinstructions can also be implemented in various forms of processorcircuitry, including programmable logic devices, field programmable gatearrays (e.g., the Xilinx Virtex series devices), field programmableobject arrays, and application specific circuits—including digital,analog and mixed analog/digital circuitry. Execution of the instructionscan be distributed among processors and/or made parallel acrossprocessors within a device or across a network of devices. Processing ofcontent signal data may also be distributed among different processorand memory devices. “Cloud” computing resources can be used as well.References to “processors,” “modules” or “components” should beunderstood to refer to functionality, rather than requiring a particularform of implementation.

Software instructions for implementing the detailed functionality can beauthored by artisans without undue experimentation from the descriptionsprovided herein, e.g., written in C, C++, Visual Basic, Java, Python,Tcl, Perl, Scheme, Ruby, etc. Cell phones and other devices according tocertain implementations of the present technology can include softwaremodules for performing the different functions and acts.

Known browser software, communications software, and media processingsoftware can be adapted for many of the uses detailed herein.

The service by which content owners ascribe certain attributes andexperiences to content (e.g., through invocation of specified software)typically uses software on the user device—either in the OS or asapplication software. Alternatively, this service can be implemented—inpart—using remote resources.

Software and hardware configuration data/instructions are commonlystored as instructions in one or more data structures conveyed bytangible media, such as magnetic or optical discs, memory cards, ROM,etc., which may be accessed across a network. Some embodiments may beimplemented as embedded systems—a special purpose computer system inwhich the operating system software and the application software isindistinguishable to the user (e.g., as is commonly the case in basiccell phones). The functionality detailed in this specification can beimplemented in operating system software, application software and/or asembedded system software.

Different of the functionality can be implemented on different devices.For example, in a system in which a smartphone communicates with aserver at a remote service provider, different tasks can be performedexclusively by one device or the other, or execution can be distributedbetween the devices. Extraction of fingerprint and watermark data fromcontent is one example of a process that can be distributed in suchfashion. Thus, it should be understood that description of an operationas being performed by a particular device (e.g., a smartphone) is notlimiting but exemplary; performance of the operation by another device(e.g., a remote server), or shared between devices, is also expresslycontemplated.

(In like fashion, description of data being stored on a particulardevice is also exemplary; data can be stored anywhere: local device,remote device, in the cloud, distributed, etc.)

In actual practice, data structures used by the present technology maybe distributed. For example, different record labels may maintain theirown data structures for music in their respective catalogs. A system mayneed to navigate a series of intermediate data structures (oftenhierarchical) to locate the one with needed information. (One suitablearrangement is detailed in Digimarc's U.S. Pat. No. 6,947,571.) Commonlyaccessed information may be cached at servers in the network—much likeDNS data—to speed access.

While this disclosure has detailed particular ordering of acts andparticular combinations of elements, it will be recognized that othercontemplated methods may re-order acts (possibly omitting some andadding others), and other contemplated combinations may omit someelements and add others, etc.

Although disclosed as complete systems, sub-combinations of the detailedarrangements are also separately contemplated.

While detailed primarily in the context of systems that perform audiocapture and processing, corresponding arrangements are equallyapplicable to systems that capture and process imagery and video, orthat capture and process multiple forms of media.

Publish/subscribe functionality can be implemented not just in a device,but across a network. An ad hoc network may be formed among users in acommon location, such as in a theatre. Content recognition informationgenerated by one user's smartphone may be published to the ad hocnetwork, and others in the network can subscribe and take action basedthereon.

Apple's Bonjour software can be used in an exemplary implementation ofsuch arrangement. Bonjour is Apple's implementation of Zeroconf—aservice discovery protocol. Bonjour locates devices on a local network,and identifies services that each offers, using multicast Domain NameSystem service records. (This software is built into the Apple Mac OS Xoperating system, and is also included in the Apple “Remote” applicationfor the iPhone—where it is used to establish connections to iTuneslibraries via WiFi.) Bonjour services are implemented at the applicationlevel largely using standard TCP/IP calls, rather than in the operatingsystem. Apple has made the source code of the Bonjour multicast DNSresponder—the core component of service discovery—available as a Darwinopen source project. The project provides source code to build theresponder daemon for a wide range of platforms, including Mac OS X,Linux, *BSD, Solaris, and Windows. In addition, Apple provides auser-installable set of services called Bonjour for Windows, as well asJava libraries. Bonjour can also be used in other embodiments of thepresent technology, involving communications between devices andsystems.

(Other software can alternatively, or additionally, be used to exchangedata between devices. Examples include Universal Plug and Play (UPnP)and its successor Devices Profile for Web Services (DPWS). These areother protocols implementing zero configuration networking services,through which devices can connect, identify themselves, advertiseavailable capabilities to other devices, share content, etc. Otherimplementations may use object request brokers, such as CORBA (aka IBMWebSphere).)

Technology for encoding/decoding watermarks is detailed, e.g., inDigimarc's U.S. Pat. Nos. 6,614,914, 6,590,996, 6,122,403, and20100150434, and in pending application Ser. No. 12/774,512; and inNielsen's U.S. Pat. Nos. 6,968,564 and 7,006,555.

Examples of audio fingerprinting are detailed in patent publications20070250716, 20070174059 and 20080300011 (Digimarc), 20080276265,20070274537 and 20050232411 (Nielsen), 20070124756 (Google), U.S. Pat.Nos. 7,516,074 (Auditude), and 6,990,453 and 7,359,889 (both Shazam).Examples of image/video fingerprinting are detailed in U.S. Pat. Nos.7,020,304 (Digimarc), 7,486,827 (Seiko-Epson), 20070253594 (Vobile),20080317278 (Thomson), and 20020044659 (NEC).

To provide a comprehensive disclosure, while complying with thestatutory requirement of conciseness, applicantsincorporate-by-reference the patent applications and other documentsreferenced herein. (Such materials are incorporated in their entireties,even if cited above in connection with specific of their teachings.)These references disclose technologies and teachings that can beincorporated into the arrangements detailed herein, and into which thetechnologies and teachings detailed herein can be incorporated. Thereader is presumed to be familiar with such prior work.

1. A method comprising: receiving, in a portable audio device, audiodata representing audio content; applying an algorithm to the receivedaudio data to derive identification data therefrom; by reference to thederived identification data, selecting a particular software program foruse with said audio content, said selecting being performed by aprocessor in the portable audio device that is configured to performsuch act; and launching said selected software program; whereinselection of the particular software program used with the audio data isbased on identification data derived from the audio data.
 2. The methodof claim 1 in which the selecting comprises selecting a particularrendering codec for the audio content, from among plural possiblerendering codecs.
 3. The method of claim 1 in which the selectingproceeds with reference to the derived identification data, and alsowith reference to context data, wherein a first software program isselected in a first context, and a second software program is selectedin a second context.
 4. The method of claim 3 in which the context datacomprises age data.
 5. The method of claim 3 in which the context datacomprises gender data.
 6. The method of claim 1 that includesdownloading the selected software program to the portable audio devicefrom a cloud repository after said selecting.
 7. The method of claim 1in which the portable audio device already includes a software programfor use with said audio content, but said selecting identifies adifferent software program, wherein the method includes downloading saiddifferent software program and launching said different softwareprogram.
 8. The method of claim 1 in which the selecting comprisesidentifying plural different software programs, and checking which ofsaid identified software programs is available in storage of theportable audio device.
 9. The method of claim 8 that includes findingthat two or more of the identified software programs are available instorage of the portable audio device, and then automatically choosingbetween the available software programs based on an algorithm.
 10. Themethod of claim 1 in which the receiving comprises receiving ambientaudio using a microphone of the portable audio device.
 11. A methodcomprising: receiving, in a portable audio device, audio datarepresenting audio content; applying an algorithm to the received audiodata to derive identification data therefrom; by reference to thederived identification data, selecting a favored software program foruse with said audio content, said selecting being performed by aprocessor in the portable device that is configured to perform such act;and indicating the favored software program to a user; wherein thefavored software program used with the audio data is selected based onidentification data derived from the audio data.
 12. The method of claim11 that includes: receiving user input identifying a software programfor use with said audio content; and if the software program identifiedby the received user input is not the favored software program, thenlimiting use of the identified software program with the audio content.13. A method comprising: receiving, in a portable audio device, audiodata representing audio content, the audio content being of a type thatcan be rendered by first software in the portable audio device; and inresponse to an attempt to render the audio content with the firstsoftware, the portable audio device indicating that second softwareshould be used instead; wherein a creator of the audio contentdesignated the second software as preferred, for that creator's audio.14. The method of claim 13 that includes refusing to render the audiocontent with the first software.
 15. The method of claim 13 thatincludes allowing only limited use of the audio content with the firstsoftware.
 16. A method comprising: with a microphone of a portabledevice, receiving audio from an ambient environment; applying analgorithm to audio data representing the received audio to deriveidentification data therefrom; by reference to the derivedidentification data, selecting a corresponding fulfillment service to beused for commerce involving the audio; wherein first received audioleads to selection of a first fulfillment service to be used forcommerce involving the first audio, and second, different received audioleads to selection of a second, different fulfillment service to be usedfor commerce involving the second audio.
 17. A method comprising:identifying audio in a user's ambient environment, the audio having beenearlier sampled by a user device; publishing information about theidentified audio, together with user context information; in response tosaid published information, receiving one or more third party bids;determining a third party associated with a winning bid; and enablingsoftware identified with the determined third party to present a buyingopportunity to the user on said user device.